Marc Kachelrieß

Purpose: While modern clinical CT scanners under normal circumstances produce high quality images, severe artifacts degrade the image quality and the diagnostic value if metal prostheses or other metal objects are present in the field of measurement. Standard methods for metal artifact reduction (MAR) replace those parts of the projection data that are affected by metal (the so‐called metal trace or metal shadow) by interpolation. However, while sinogram interpolation methods efficiently remove metal artifacts, new artifacts are often introduced, as interpolation cannot completely recover the information from the metal trace. The purpose of this work is to introduce a generalized normalization technique for MAR, allowing for efficient reduction of metal artifacts while adding almost no new ones. The method presented is compared to a standard MAR method, as well as MAR using simple length normalization. Methods: In the first step, metal is segmented in the image domain by thresholding. A 3D forward projection identifies the metal trace in the original projections. Before interpolation, the projections are normalized based on a 3D forward projection of a prior image. This prior image is obtained, for example, by a multithreshold segmentation of the initial image. The original rawdata are divided by the projection data of the prior image and, after interpolation, denormalized again. Simulations and measurements are performed to compare normalized metal artifact reduction (NMAR) to standard MAR with linear interpolation and MAR based on simple length normalization. Results: Promising results for clinical spiral cone‐beam data are presented in this work. Included are patients with hip prostheses, dental fillings, and spine fixation, which were scanned at pitch values ranging from 0.9 to 3.2. Image quality is improved considerably, particularly for metal implants within bone structures or in their proximity. The improvements are evaluated by comparing profiles through images and sinograms for the different methods and by inspecting ROIs. NMAR outperforms both other methods in all cases. It reduces metal artifacts to a minimum, even close to metal regions. Even for patients with dental fillings, which cause most severe artifacts, satisfactory results are obtained with NMAR. In contrast to other methods, NMAR prevents the usual blurring of structures close to metal implants if the metal artifacts are moderate. Conclusions: NMAR clearly outperforms the other methods for both moderate and severe artifacts. The proposed method reliably reduces metal artifacts from simulated as well as from clinical CT data. Computationally efficient and inexpensive compared to iterative methods, NMAR can be used as an additional step in any conventional sinogram inpainting‐based MAR method.

Background —We investigated the applicability and image quality of contrast-enhanced coronary artery visualization by multislice spiral CT using retrospective ECG gating. Methods and Results —Twenty-five patients in sinus rhythm (significant coronary artery stenoses ruled out by invasive angiography) were studied with a multislice spiral CT (Siemens SOMATOM Volume Zoom). In inspiration (mean breathhold, 37 seconds), a volume data set of the heart was acquired (intravenous contrast agent; 4×1-mm slice thickness; 500-ms rotation; table feed, 1.5 mm/360°). Simultaneous recording of the ECG permitted retrospective reconstruction of contiguous cross sections in intervals of 1 mm at any desired interval of the cardiac cycle. The mean duration of the image reconstruction window was 185 ms. Next to 3-dimensional reconstructions of the heart and coronary arteries, multiplanar reconstructions were rendered to determine the visualized length of the coronary arteries, the contrast-to-noise ratio, and the correlation of coronary artery diameters to quantitative coronary angiography. Conclusions —The coronary arteries could be visualized over long segments (left main, 9±4 mm; left anterior descending, 112±34 mm; left circumflex, 80±29 mm; right coronary artery, 116±33 mm). On average, 78±16% of these distances were visualized free of motion artifacts. The mean contrast-to-noise ratio was 9.3±3.3. Coronary artery diameters in multislice spiral CT showed close correlation to quantitative coronary angiography (CT, 3.3±1.0 mm; angiography, 3.2±0.9 mm; mean difference, 0.38 mm; r =0.86). Contrast-enhanced multislice spiral CT permits visualization of the coronary artery lumen. Further studies are necessary to determine whether image quality is sufficient to reliably detect coronary artery stenoses.

In computed tomography there are different situations where reconstruction has to be performed with limited raw data. In the past few years it has been shown that algorithms which are based on compressed sensing theory are able to handle incomplete datasets quite well. As a cost function these algorithms use the ℓ(1)-norm of the image after it has been transformed by a sparsifying transformation. This yields to an inequality-constrained convex optimization problem. Due to the large size of the optimization problem some heuristic optimization algorithms have been proposed in the past few years. The most popular way is optimizing the raw data and sparsity cost functions separately in an alternating manner. In this paper we will follow this strategy and present a new method to adapt these optimization steps. Compared to existing methods which perform similarly, the proposed method needs no a priori knowledge about the raw data consistency. It is ensured that the algorithm converges to the lowest possible value of the raw data cost function, while holding the sparsity constraint at a low value. This is achieved by transferring the step-size determination of both optimization procedures into the raw data domain, where they are adapted to each other. To evaluate the algorithm, we process measured clinical datasets. To cover a wide field of possible applications, we focus on the problems of angular undersampling, data lost due to metal implants, limited view angle tomography and interior tomography. In all cases the presented method reaches convergence within less than 25 iteration steps, while using a constant set of algorithm control parameters. The image artifacts caused by incomplete raw data are mostly removed without introducing new effects like staircasing. All scenarios are compared to an existing implementation of the ASD-POCS algorithm, which realizes the step-size adaption in a different way. Additional prior information as proposed by the PICCS algorithm can be incorporated easily into the optimization process.

The problem of metal artifact reduction (MAR) is almost as old as the clinical use of computed tomography itself. When metal implants are present in the field of measurement, severe artifacts degrade the image quality and the diagnostic value of CT images. Up to now, no generally accepted solution to this issue has been found. In this work, a method based on a new MAR concept is presented: frequency split metal artifact reduction (FSMAR). It ensures efficient reduction of metal artifacts at high image quality with enhanced preservation of details close to metal implants.FSMAR combines a raw data inpainting-based MAR method with an image-based frequency split approach. Many typical methods for metal artifact reduction are inpainting-based MAR methods and simply replace unreliable parts of the projection data, for example, by linear interpolation. Frequency split approaches were used in CT, for example, by combining two reconstruction methods in order to reduce cone-beam artifacts. FSMAR combines the high frequencies of an uncorrected image, where all available data were used for the reconstruction with the more reliable low frequencies of an image which was corrected with an inpainting-based MAR method. The algorithm is tested in combination with normalized metal artifact reduction (NMAR) and with a standard inpainting-based MAR approach. NMAR is a more sophisticated inpainting-based MAR method, which introduces less new artifacts which may result from interpolation errors. A quantitative evaluation was performed using the examples of a simulation of the XCAT phantom and a scan of a spine phantom. Further evaluation includes patients with different types of metal implants: hip prostheses, dental fillings, neurocoil, and spine fixation, which were scanned with a modern clinical dual source CT scanner.FSMAR ensures sharp edges and a preservation of anatomical details which is in many cases better than after applying an inpainting-based MAR method only. In contrast to other MAR methods, FSMAR yields images without the usual blurring close to implants.FSMAR should be used together with NMAR, a combination which ensures an accurate correction of both high and low frequencies. The algorithm is computationally inexpensive compared to iterative methods and methods with complex inpainting schemes. No parameters were chosen manually; it is ready for an application in clinical routine.

To study the performance of different dual energy computed tomography (DECT) techniques, which are available today, and future multi energy CT (MECT) employing novel photon counting detectors in an image-based material decomposition task.The material decomposition performance of different energy-resolved CT acquisition techniques is assessed and compared in a simulation study of virtual non-contrast imaging and iodine quantification. The material-specific images are obtained via a statistically optimal image-based material decomposition. A projection-based maximum likelihood approach was used for comparison with the authors' image-based method. The different dedicated dual energy CT techniques are simulated employing realistic noise models and x-ray spectra. The authors compare dual source DECT with fast kV switching DECT and the dual layer sandwich detector DECT approach. Subsequent scanning and a subtraction method are studied as well. Further, the authors benchmark future MECT with novel photon counting detectors in a dedicated DECT application against the performance of today's DECT using a realistic model. Additionally, possible dual source concepts employing photon counting detectors are studied.The DECT comparison study shows that dual source DECT has the best performance, followed by the fast kV switching technique and the sandwich detector approach. Comparing DECT with future MECT, the authors found noticeable material image quality improvements for an ideal photon counting detector; however, a realistic detector model with multiple energy bins predicts a performance on the level of dual source DECT at 100 kV/Sn 140 kV. Employing photon counting detectors in dual source concepts can improve the performance again above the level of a single realistic photon counting detector and also above the level of dual source DECT.Substantial differences in the performance of today's DECT approaches were found for the application of virtual non-contrast and iodine imaging. Future MECT with realistic photon counting detectors currently can only perform comparably to dual source DECT at 100 kV/Sn 140 kV. Dual source concepts with photon counting detectors could be a solution to this problem, promising a better performance.

Abstract The advent of computed tomography (CT) has revolutionized radiology, and this revolution is still going on. Starting as a pure head scanner, modern CT systems are now able to perform whole-body examinations within a couple of seconds in isotropic resolution, single-rotation whole-organ perfusion, and temporal resolution to fulfill the needs of cardiac CT. Because of the increasing number of CT examinations in all age groups and overall medical-driven radiation exposure, dose reduction remains a hot topic. Although fast gantry rotation, broad detector arrays, and different dual-energy solutions were main topics in the past years, new techniques such as photon counting detectors, powerful x-ray tubes for low-kV scanning, automated image preprocessing, and machine learning algorithms have moved into focus today. The aim of this article is to give an overview of the technical specifications of up-to-date available CT systems and recent hardware and software innovations for CT systems in the near future.

Abstract Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality.

In modern computed tomography (CT) there is a strong desire to reduce patient dose and/or to improve image quality by increasing spatial resolution and decreasing image noise. These are conflicting demands since increasing resolution at a constant noise level or decreasing noise at a constant resolution level implies a higher demand on x-ray power and an increase of patient dose. X-ray tube power is limited due to technical reasons. We therefore developed a generalized multi-dimensional adaptive filtering approach that applies nonlinear filters in up to three dimensions in the raw data domain. This new method differs from approaches in the literature since our nonlinear filters are applied not only in the detector row direction but also in the view and in the z-direction. This true three-dimensional filtering improves the quantum statistics of a measured projection value proportional to the third power of the filter size. Resolution tradeoffs are shared among these three dimensions and thus are considerably smaller as compared to one-dimensional smoothing approaches. Patient data of spiral and sequential single- and multi-slice CT scans as well as simulated spiral cone-beam data were processed to evaluate these new approaches. Image quality was assessed by evaluation of difference images, by measuring the image noise and the noise reduction, and by calculating the image resolution using point spread functions. The use of generalized adaptive filters helps to reduce image noise or, alternatively, patient dose. Image noise structures, typically along the direction of the highest attenuation, are effectively reduced. Noise reduction values of typically 30%-60% can be achieved in noncylindrical body regions like the shoulder. The loss in image resolution remains below 5% for all cases. In addition, the new method has a great potential to reduce metal artifacts, e.g., in the hip region.

Subsecond spiral computed tomography (CT) offers great potential for improving heart imaging. The new multi-row detector technology adds significantly to this potential. We therefore developed and validated dedicated cardiac reconstruction algorithms for imaging the heart with subsecond multi-slice spiral CT utilizing electrocardiogram (ECG) information. The single-slice cardiac z-interpolation algorithms 180 degrees CI and 180 degrees CD [Med. Phys. 25, 2417-2431 (1998)] were generalized to allow imaging of the heart for M-slice scanners. Two classes of algorithms were investigated: 180 degrees MCD (multi-slice cardio delta), a partial scan reconstruction of 180 degrees + delta data with a < phi (fan angle) resulting in effective scan times of 250 ms (central ray) when a 0.5 s rotation mode is available, and 180 degrees MCI (multi-slice cardio interpolation), a piecewise weighted interpolation between successive spiral data segments belonging to the same heart phase, potentially providing a relative temporal resolution of 12.5% of the heart cycle when a four-slice scanner is used and the table increment is chosen to be greater than or equal to the collimated slice thickness. Data segments are selected by correlation with the simultaneously recorded ECG signal. Theoretical studies, computer simulations, as well as patient measurements were carried out for a multi-slice scanner providing M = 4 slices to evaluate these new approaches and determine the optimal scan protocol. Both algorithms, 180 degrees MCD and 180 degrees MCI, provide significant improvements in image quality, including extremely arythmic cases. Artifacts in the reconstructed images as well as in 3D displays such as multiplanar reformations were largely reduced as compared to the standard z-interpolation algorithm 180 degrees MLI (multi-slice linear interpolation). Image quality appears adequate for precise calcium scoring and CT angiography of the coronary arteries with conventional subsecond multislice spiral CT. It turned out that for heart rates fH > or = 70 min(-1) the partial scan approach 180 degrees MCD yields unsatisfactory results as compared to 180 degrees MCI. Our theoretical considerations show that a freely selectable scanner rotation time chosen as a function of the patient's heart rate, would further improve the relative temporal resolution and thus further reduce motion artifacts. In our case an additional 0.6 s mode besides the available 0.5 s mode would be very helpful. Moreover, if technically feasible, lower rotation times such as 0.3 s or even less would result in improved image quality. The use of multi-slice techniques for cardiac CT together with the new z-interpolation methods improves the quality of heart imaging significantly. The high temporal resolution of 180 degrees MCI is adequate for spatial and temporal tracking of anatomic structures of the heart (4D reconstruction).

Subsecond computed tomography (CT) scanning offers potential for improved heart imaging. We therefore developed and validated dedicated reconstruction algorithms for imaging the heart with subsecond spiral CT utilizing electrocardiogram (ECG) information. We modified spiral CT z ‐interpolation algorithms on a subsecond spiral CT scanner. Two new classes of algorithms were investigated: (a) 180°CI (cardio interpolation), a piecewise linear interpolation between adjacent spiral data segments belonging to the same heart phase where segments are selected by correlation with the simultaneously recorded ECG signal and (b) 180°CD (cardio delta), a partial scan reconstruction of with angle, resulting in reduced effective scan times of less than 0.5 s. Computer simulations as well as processing of clinical data collected with 0.75 s scan time were carried out to evaluate these new approaches. Both 180°CI and 180°CD provided significant improvements in image quality. Motion artifacts in the reconstructed images were largely reduced as compared to standard spiral reconstructions; in particular, coronary calcifications were delineated more sharply and multiplanar reformations showed improved contiguity. However, new artifacts in the image plane are introduced, mostly due to the combination of different data segments. ECG‐oriented image reconstructions improve the quality of heart imaging with spiral CT significantly. Image quality and the display of coronary calcification appear adequate to assess coronary calcium measurements with conventional subsecond spiral CT.

Dual energy CT (DECT) measures the object of interest using two different x-ray spectra in order to provide energy-selective CT images or in order to get the material decomposition of the object. Today, two decomposition techniques are known. Image-based DECT uses linear combinations of reconstructed images to get an image that contains material-selective DECT information. Rawdata-based DECT correctly treats the available information by passing the rawdata through a decomposition function that uses information from both rawdata sets to create DECT specific (e.g., material-selective) rawdata. Then the image reconstruction yields material-selective images. Rawdata-based image decomposition generally obtains better image quality; however, it needs matched rawdata sets. This means that physically the same lines need to be measured for each spectrum. In today's CT scanners, this is not the case. The authors propose a new image-based method to combine mismatched rawdata sets for DECT information. The method allows for implementation in a scanner's rawdata precorrection pipeline or may be used in image domain. They compare the ability of the three methods (image-based standard method, proposed method, and rawdata-based standard method) to perform material decomposition and to provide monochromatic images. Thereby they use typical clinical and preclinical scanner arrangements including circular cone-beam CT and spiral CT. The proposed method is found to perform better than the image-based standard method and is inferior to the rawdata-based method. However, the proposed method can be used with the frequent case of mismatched data sets that exclude rawdata-based methods.

Purpose: Due to x‐ray beam polychromaticity and scattered radiation, attenuation measurements tend to be underestimated. Cupping and beam hardening artifacts become apparent in the reconstructed CT images. If only one material such as water, for example, is present, these artifacts can be reduced by precorrecting the rawdata. Higher order beam hardening artifacts, as they result when a mixture of materials such as water and bone, or water and bone and iodine is present, require an iterative beam hardening correction where the image is segmented into different materials and those are forward projected to obtain new rawdata. Typically, the forward projection must correctly model the beam polychromaticity and account for all physical effects, including the energy dependence of the assumed materials in the patient, the detector response, and others. We propose a new algorithm that does not require any knowledge about spectra or attenuation coefficients and that does not need to be calibrated. The proposed method corrects beam hardening in single energy CT data. Methods: The only a priori knowledge entering EBHC is the segmentation of the object into different materials. Materials other than water are segmented from the original image, e.g., by using simple thresholding. Then, a (monochromatic) forward projection of these other materials is performed. The measured rawdata and the forward projected material‐specific rawdata are monomially combined (e.g., multiplied or squared) and reconstructed to yield a set of correction volumes. These are then linearly combined and added to the original volume. The combination weights are determined to maximize the flatness of the new and corrected volume. EBHC is evaluated using data acquired with a modern cone‐beam dual‐source spiral CT scanner (Somatom Definition Flash, Siemens Healthcare, Forchheim, Germany), with a modern dual‐source micro‐CT scanner (TomoScope Synergy Twin, CT Imaging GmbH, Erlangen, Germany), and with a modern C‐arm CT scanner (Axiom Artis dTA, Siemens Healthcare, Forchheim, Germany). A large variety of phantom, small animal, and patient data were used to demonstrate the data and system independence of EBHC. Results: Although no physics apart from the initial segmentation procedure enter the correction process, beam hardening artifacts were significantly reduced by EBHC. The image quality for clinical CT, micro‐CT, and C‐arm CT was highly improved. Only in the case of C‐arm CT, where high scatter levels and calibration errors occur, the relative improvement was smaller. Conclusions: The empirical beam hardening correction is an interesting alternative to conventional iterative higher order beam hardening correction algorithms. It does not tend to over‐ or undercorrect the data. Apart from the segmentation step, EBHC does not require assumptions on the spectra or on the type of material involved. Potentially, it can therefore be applied to any CT image.

The advent of computed tomography (CT) has revolutionized radiology. Starting as head-only scanners, modern CT systems are now capable of performing whole-body examinations within a couple of seconds in isotropic resolution. Technical advancements of scanner hardware and image reconstruction techniques are reviewed and discussed in their clinical context. These improvements have led to a steady increase of CT examinations in all age groups for a number of reasons. On the one hand, it is very easy today to obtain whole-body data for oncologic staging and follow-up or for trauma imaging. On the other hand, new examinations such as cardiac imaging, virtual colonoscopy, gout imaging, and whole-organ perfusion imaging have widened the application profile of CT. The increasing awareness of risks associated with radiation exposure triggered the development of a variety of dose reduction techniques. Effective dose values below 1 mSv, less than the annual natural background radiation (3.1 mSv/year on average in the United States), are now routinely possible for a number of dedicated examinations, even for coronary CT angiography.

Purpose: Dual Energy CT (DECT) provides so‐called monoenergetic images based on a linear combination of the original polychromatic images. At certain patient‐specific energy levels, corresponding to certain patient‐ and slice‐dependent linear combination weights, e.g., E = 160 keV corresponds to α = 1.57, a significant reduction of metal artifacts may be observed. The authors aimed at analyzing the method for its artifact reduction capabilities to identify its limitations. The results are compared with raw data‐based processing. Methods: Clinical DECT uses a simplified version of monochromatic imaging by linearly combining the low and the high kV images and by assigning an energy to that linear combination. Those pseudo‐monochromatic images can be used by radiologists to obtain images with reduced metal artifacts. The authors analyzed the underlying physics and carried out a series expansion of the polychromatic attenuation equations. The resulting nonlinear terms are responsible for the artifacts, but they are not linearly related between the low and the high kV scan: A linear combination of both images cannot eliminate the nonlinearities, it can only reduce their impact. Scattered radiation yields additional noncanceling nonlinearities. This method is compared to raw data‐based artifact correction methods. To quantify the artifact reduction potential of pseudo‐monochromatic images, they simulated the FORBILD abdomen phantom with metal implants, and they assessed patient data sets of a clinical dual source CT system (100, 140 kV Sn) containing artifacts induced by a highly concentrated contrast agent bolus and by metal. In each case, they manually selected an optimal α and compared it to a raw data‐based material decomposition in case of simulation, to raw data‐based material decomposition of inconsistent rays in case of the patient data set containing contrast agent, and to the frequency split normalized metal artifact reduction in case of the metal implant. For each case, the contrast‐to‐noise ratio (CNR) was assessed. Results: In the simulation, the pseudo‐monochromatic images yielded acceptable artifact reduction results. However, the CNR in the artifact‐reduced images was more than 60% lower than in the original polychromatic images. In contrast, the raw data‐based material decomposition did not significantly reduce the CNR in the virtual monochromatic images. Regarding the patient data with beam hardening artifacts and with metal artifacts from small implants the pseudo‐monochromatic method was able to reduce the artifacts, again with the downside of a significant CNR reduction. More intense metal artifacts, e.g., as those caused by an artificial hip joint, could not be suppressed. Conclusions: Pseudo‐monochromatic imaging is able to reduce beam hardening, scatter, and metal artifacts in some cases but it cannot remove them. In all cases, the CNR is significantly reduced, thereby rendering the method questionable, unless special post processing algorithms are implemented to restore the high CNR from the original images (e.g., by using a frequency split technique). Raw data‐based dual energy decomposition methods should be preferred, in particular, because the CNR penalty is almost negligible.

X-ray scatter is a major cause of image quality degradation in dimensional CT. Especially, in case of highly attenuating components scatter-to-primary ratios may easily be higher than 1. The corresponding artifacts which appear as cupping or dark streaks in the CT reconstruction may impair a metrological assessment. Therefore, an appropriate scatter correction is crucial. Thereby, the gold standard is to predict the scatter distribution using a Monte Carlo (MC) code and subtract the corresponding scatter estimate from the measured raw data. MC, however, is too slow to be used routinely. To correct for scatter in real-time, we developed the deep scatter estimation (DSE). It uses a deep convolutional neural network which is trained to reproduce the output of MC simulations using only the acquired projection data as input. Once trained, DSE can be applied in real-time. The present study demonstrates the potential of the proposed approach using simulations and measurements. In both cases the DSE yields highly accurate scatter estimates that differ by< 3% from our MC scatter predictions. Further, DSE clearly outperforms kernel-based scatter estimation techniques and hybrid approaches, as they are in use today.

To develop four-dimensional (4D) respiratory time-resolved MRI based on free-breathing acquisition of radial MR data with very high undersampling.We propose the 4D joint motion-compensated high-dimensional total variation (4D joint MoCo-HDTV) algorithm, which alternates between motion-compensated image reconstruction and artifact-robust motion estimation at multiple resolution levels. The algorithm is applied to radial MR data of the thorax and upper abdomen of 12 free-breathing subjects with acquisition times between 37 and 41 s and undersampling factors of 16.8. Resulting images are compared with compressed sensing-based 4D motion-adaptive spatio-temporal regularization (MASTeR) and 4D high-dimensional total variation (HDTV) reconstructions.For all subjects, 4D joint MoCo-HDTV achieves higher similarity in terms of normalized mutual information and cross-correlation than 4D MASTeR and 4D HDTV when compared with reference 4D gated gridding reconstructions with 8.4 ± 1.1 times longer acquisition times. In a qualitative assessment of artifact level and image sharpness by two radiologists, 4D joint MoCo-HDTV reveals higher scores (P < 0.05) than 4D HDTV and 4D MASTeR at the same undersampling factor and the reference 4D gated gridding reconstructions, respectively.4D joint MoCo-HDTV enables time-resolved image reconstruction of free-breathing radial MR data with undersampling factors of 16.8 while achieving low-streak artifact levels and high image sharpness. Magn Reson Med 77:1170-1183, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Purpose X‐ray scattering leads to CT images with a reduced contrast, inaccurate CT values as well as streak and cupping artifacts. Therefore, scatter correction is crucial to maintain the diagnostic value of CT and CBCT examinations. However, existing approaches are not able to combine both high accuracy and high computational performance. Therefore, we propose the deep scatter estimation (DSE): a deep convolutional neural network to derive highly accurate scatter estimates in real time. Methods Gold standard scatter estimation approaches rely on dedicated Monte Carlo (MC) photon transport codes. However, being computationally expensive, MC methods cannot be used routinely. To enable real‐time scatter correction with similar accuracy, DSE uses a deep convolutional neural network that is trained to predict MC scatter estimates based on the acquired projection data. Here, the potential of DSE is demonstrated using simulations of CBCT head, thorax, and abdomen scans as well as measurements at an experimental table‐top CBCT. Two conventional computationally efficient scatter estimation approaches were implemented as reference: a kernel‐based scatter estimation (KSE) and the hybrid scatter estimation (HSE). Results The simulation study demonstrates that DSE generalizes well to varying tube voltages, varying noise levels as well as varying anatomical regions as long as they are appropriately represented within the training data. In any case the deviation of the scatter estimates from the ground truth MC scatter distribution is less than 1.8% while it is between 6.2% and 293.3% for HSE and between 11.2% and 20.5% for KSE. To evaluate the performance for real data, measurements of an anthropomorphic head phantom were performed. Errors were quantified by a comparison to a slit scan reconstruction. Here, the deviation is 278 HU (no correction), 123 HU (KSE), 65 HU (HSE), and 6 HU (DSE), respectively. Conclusions The DSE clearly outperforms conventional scatter estimation approaches in terms of accuracy. DSE is nearly as accurate as Monte Carlo simulations but is superior in terms of speed (≈10 ms/projection) by orders of magnitude.

Objectives Reconstructing images from measurements with small pixels below the system's resolution limit theoretically results in image noise reduction compared with measurements with larger pixels. We evaluate and quantify this effect using data acquired with the small pixels of a photon-counting (PC) computed tomography scanner that can be operated in different detector pixel binning modes and with a conventional energy-integrating (EI) detector. Materials and Methods An anthropomorphic abdominal phantom that can be extended to 3 sizes by adding fat extension rings, equipped with iodine inserts as well as human cadavers, was measured at tube voltages ranging from 80 to 140 kV. The images were acquired with the EI detector (0.6 mm pixel size at isocenter) and the PC detector operating in Macro mode (0.5 mm pixel size at iso) and ultrahigh-resolution (UHR) mode (0.25 mm pixel size at iso). Both detectors are components of the same dual-source prototype computed tomography system. During reconstruction, the modulation transfer functions were matched to the one of the EI detector. The dose-normalized contrast-to-noise ratio (CNRD) values are evaluated as a figure of merit. Results Images acquired in UHR mode achieve on average approximately 6% higher CNRD compared with Macro mode at the same spatial resolution for a quantitative D40f kernel. Using a sharper B70f kernel, the improvement increases to 21% on average. In addition, the better performance of PC detectors compared with EI detectors with regard to iodine imaging has been evaluated by comparing CNRD values for Macro and EI. Combining both of these effects, a CNRD improvement of up to 34%, corresponding to a potential dose reduction of up to 43%, can be achieved for D40f. Conclusions Reconstruction of UHR data with a modulation transfer function below the system's resolution limit reduces image noise for all patient sizes and tube voltages compared with standard acquisitions. Thus, a relevant dose reduction may be clinically possible while maintaining image quality.

To achieve higher volume coverage at improved z-resolution in computed tomography (CT), systems with a large number of detector rows are demanded. However, handling an increased number of detector rows, as compared to today's four-slice scanners, requires to accounting for the cone geometry of the beams. Many so-called cone-beam reconstruction algorithms have been proposed during the last decade. None met all the requirements of the medical spiral cone-beam CT in regard to the need for high image quality, low patient dose and low reconstruction times. We therefore propose an approximate cone-beam algorithm which uses virtual reconstruction planes tilted to optimally fit 180 degrees spiral segments, i.e., the advanced single-slice rebinning (ASSR) algorithm. Our algorithm is a modification of the single-slice rebinning algorithm proposed by Noo et al. [Phys. Med. Biol. 44, 561-570 (1999)] since we use tilted reconstruction slices instead of transaxial slices to approximate the spiral path. Theoretical considerations as well as the reconstruction of simulated phantom data in comparison to the gold standard 180 degrees LI (single-slice spiral CT) were carried out. Image artifacts, z-resolution as well as noise levels were evaluated for all simulated scanners. Even for a high number of detector rows the artifact level in the reconstructed images remains comparable to that of 180 degrees LI. Multiplanar reformations of the Defrise phantom show none of the typical cone-beam artifacts usually appearing when going to larger cone angles. Image noise as well as the shape of the respective slice sensitivity profiles are equivalent to the single-slice spiral reconstruction, z-resolution is slightly decreased. The ASSR has the potential to become a practical tool for medical spiral cone-beam CT. Its computational complexity lies in the order of standard single-slice CT and it allows to use available 2D backprojection hardware.

We present a new high-precision method for the geometric calibration in cone-beam computed tomography. It is based on a Fourier analysis of the projection-orbit data, recorded with a flat-panel area detector, of individual point-like objects. For circular scan trajectories the complete set of misalignment parameters which determine the deviation of the detector alignment from the ideal scan geometry are obtained from explicit analytic expressions. To derive these expressions we show how to disentangle the problems of calculating misalignment parameters and point coordinates. The calculation of the coordinates of the point objects inside the scanned volume, in units of the distance from the focal spot to the center of rotation, is then possible analytically likewise. We simulate point-projection data on a misaligned detector with various amounts of randomness added to mimic measurement uncertainties. This data is then employed in our calibration to validate the method by comparing the resulting misalignment parameters and point coordinates to the known true ones. We also present our implementation and results for the geometric calibration of micro-CT systems. The effectiveness of the corresponding misalignment correction in reducing image artifacts is exemplified by reconstructed micro-CT images.

We propose an empirical cupping correction (ECC) algorithm to correct for CT cupping artifacts that are induced by nonlinearities in the projection data. The method is raw data based, empirical, and requires neither knowledge of the x‐ray spectrum nor of the attenuation coefficients. It aims at linearizing the attenuation data using a precorrection function of polynomial form. The coefficients of the polynomial are determined once using a calibration scan of a homogeneous phantom. Computing the coefficients is done in image domain by fitting a series of basis images to a template image. The template image is obtained directly from the uncorrected phantom image and no assumptions on the phantom size or of its positioning are made. Raw data are precorrected by passing them through the once‐determined polynomial. As an example we demonstrate how ECC can be used to perform water precorrection for an in vivo micro‐CT scanner (TomoScope 30 s, VAMP GmbH, Erlangen, Germany). For this particular case, practical considerations regarding the definition of the template image are given. ECC strives to remove the cupping artifacts and to obtain well‐calibrated CT values. Although ECC is a first‐order correction and cannot compete with iterative higher‐order beam hardening or scatter correction algorithms, our in vivo mouse images show a significant reduction of bone‐induced artifacts as well. A combination of ECC with analytical techniques yielding a hybrid cupping correction method is possible and allows for channel‐dependent correction functions.

X-ray computerised tomography (CT) scanning with continuous patient transport has been established under the name Spiral CT since several years as the standard clinical examination procedure. This technique has been improved continuously with respect to scan speed, temporal response and z-axis resolution by the use of latest technical developments: Rotation times up to 0.5 s and multi-row detector array systems. Today detector systems with M + 4 simultaneously measured slices are available. We report about recent progress of spiral CT reconstruction algorithms that are based on multi-slice data. It is demonstrated that the new technology not only provides significant reduction in overall scan times and thereby of the CT scanner?s X-ray tube load; beyond that, the new technology allows CT imaging of the beating heart with high level image quality in standard clinical routine.

Material‐selective imaging using dual energy CT (DECT) relies heavily on well‐calibrated material decomposition functions. These require the precise knowledge of the detected x‐ray spectra, and even if they are exactly known the reliability of DECT will suffer from scattered radiation. We propose an empirical method to determine the proper decomposition function. In contrast to other decomposition algorithms our empirical dual energy calibration (EDEC) technique requires neither knowledge of the spectra nor of the attenuation coefficients. The desired material‐selective raw data and are obtained as functions of the measured attenuation data and (one DECT raw data sets) by passing them through a polynomial function. The polynomial's coefficients are determined using a general least squares fit based on thresholded images of a calibration phantom. The calibration phantom's dimension should be of the same order of magnitude as the test object, but other than that no assumptions on its exact size or positioning are made. Once the decomposition coefficients are determined DECT raw data can be decomposed by simply passing them through the polynomial. To demonstrate EDEC simulations of an oval CTDI phantom, a lung phantom, a thorax phantom and a mouse phantom were carried out. The method was further verified by measuring a physical mouse phantom, a half‐and‐half‐cylinder phantom and a Yin‐Yang phantom with a dedicated in vivo dual source micro‐CT scanner. The raw data were decomposed into their components, reconstructed, and the pixel values obtained were compared to the theoretical values. The determination of the calibration coefficients with EDEC is very robust and depends only slightly on the type of calibration phantom used. The images of the test phantoms (simulations and measurements) show a nearly perfect agreement with the theoretical values and density values. Since EDEC is an empirical technique it inherently compensates for scatter components. The empirical dual energy calibration technique is a pragmatic, simple, and reliable calibration approach that produces highly quantitative DECT images.

Tomographic image reconstruction, such as the reconstruction of computed tomography projection values, of tomosynthesis data, positron emission tomography or SPECT events, and of magnetic resonance imaging data is computationally very demanding. One of the most time‐consuming steps is the backprojection. Recently, a novel general purpose architecture optimized for distributed computing became available: the cell broadband engine (CBE). To maximize image reconstruction speed we modified our parallel‐beam backprojection algorithm [two dimensional (2D)] and our perspective backprojection algorithm [three dimensional (3D), cone beam for flat–panel detectors] and optimized the code for the CBE. The algorithms are pixel or voxel driven, run with floating point accuracy and use linear (LI) or nearest neighbor (NN) interpolation between detector elements. For the parallel‐beam case, 512 projections per half rotation, 1024 detector channels, and an image of size was used. The cone‐beam backprojection performance was assessed by backprojecting a full circle scan of 512 projections of size into a volume of size voxels. The field of view was chosen to completely lie within the field of measurement and the pixel or voxel size was set to correspond to the detector element size projected to the center of rotation divided by . Both the PC and the CBE were clocked at . For the parallel backprojection of 512 projections into a image, a throughput of (LI) and (NN) was measured on the PC, whereas the CBE achieved (LI) and (NN), respectively. The cone‐beam backprojection of 512 projections into the volume took on the PC and is as fast as on the cell. Thereby, the cell greatly outperforms today's top‐notch backprojections based on graphical processing units. Using both CBEs of our dual cell‐based blade (Mercury Computer Systems) allows to 2D backproject 330 images/s and one can complete the 3D cone‐beam backprojection in .

Dual energy CT (DECT) allows calculating images that show the spatial distribution of the electron density and the atomic number or, more common, images of two basis material densities. In contrast, the Hounsfield unit that is shown in standard CT images is a measure of the x-ray attenuation, which is a function of the atomic number and electron density. To acquire additional information, DECT measures the object of interest using two different detected x-ray spectra. Most clinical CT scanners realize dual energy CT by fast tube voltage switching or by dual source dual detector arrangements and therefore do not allow measuring geometrically identical lines with each spectrum. Then, it is not possible to preprocess the raw data and calculate dual energy-specific raw data sets. The combination of the information of both spectra rather needs to be carried out in image domain after image reconstruction. Compared to the ideal raw data-based dual energy approaches, those image-based DECT methods are inferior because they are not able to correctly deal with the polychromatic nature of the x-rays. This article proposes a dedicated dual energy reconstruction algorithm for inconsistent rays that correctly accounts for all spectral effects.Material decomposition from inconsistent rays (MDIR) is an iterative method to indirectly perform raw data-based DECT even though different lines were measured for both spectra. Its iterative nature allows treating the x-ray polychromaticity correctly. The iterative process is initialized by density images that were obtained from an image-based material decomposition. Those images suffer from errors that originate from the polychromatic nature of the spectra. These errors are calculated by polychromatic forward projection of each measured line. After correction of the initial material density images, the polychromatic forward projection is repeated with more accurate material density images, yielding a more accurate error calculation. To demonstrate the proposed method, simulations and measurements were performed using clinical and preclinical dual source dual energy CT scanners.Two iterations of MDIR are sufficient to greatly improve the qualitative and quantitative information in material density images. It is shown that banding artifacts, cupping artifacts, and mean density errors can be completely eliminated. Simulations with high geometrical inconsistency between the rays of different spectra indicate that nearly exact material decomposition is possible with MDIR. Furthermore, simulations show that the method works well in the presence of materials with K-edges within the detected spectrum. Phantom measurements using a clinical dual source CT scanner show the elimination of artifacts, which cause up to 4% mean density error.At moderate computational burden, the proposed MDIR algorithm yields images of the same high quality as direct raw data-based DECT methods. In contrast to those, MDIR is applicable to the case of inconsistent rays, as it often occurs in clinical or preclinical CT. Compared to image-based methods MDIR reduces artifacts and improves mean density errors in material density images. All dual energy postprocessing methods that are in use today, such as bone removal, virtual noncontrast images, etc., can be applied to the images provided by MDIR.

To evaluate several algorithms for 4D cone-beam computed tomography (4D CBCT) with slow rotating devices. 4D CBCT is used to perform phase-correlated (PC) reconstructions of moving objects, such as breathing patients, for example. Such motion phase-dependent reconstructions are especially useful for updating treatment plans in radiation therapy. The treatment plan can be registered more precisely to the motion of the tumor and, in consequence, the irradiation margins for the treatment, the so-called planning target volume, can be reduced significantlyIn the study, several algorithms were evaluated for kilovoltage cone-beam CT units attached to linear particle accelerators. The reconstruction algorithms were the conventional PC reconstruction, the McKinnon-Bates (MKB) algorithm, the prior image constrained compressed sensing (PICCS) approach, a total variation minimization (ASD-POCS) algorithm, and the auto-adaptive phase correlation (AAPC) algorithm. For each algorithm, the same motion-affected raw data were used, i.e., one simulated and one measured data set. The reconstruction results from the authors' implementation of these algorithms were evaluated regarding their noise and artifact levels, their residual motion blur, and their computational complexity and convergence.In general, it turned out that the residual motion blur was lowest in those algorithms which exclusively use data from a single motion phase. Algorithms using the image from the full data set as initialization or as a reference for the reconstruction were not capable of fully removing the motion blurring. The iterative algorithms, especially approaches based on total variation minimization, showed lower noise and artifact levels but were computationally complex. The conventional methods based on a single filtered backprojection were computationally inexpensive but suffered from higher noise and streak artifacts which limit the usability. In contrast, these methods showed to be less demanding and more predictable in their outcome than the total variation minimization based approaches.The reconstruction algorithms including at least one iterative step can reduce the 4 CBCT specific artifacts. Nevertheless, the algorithms that use the full data set, at least for initialization, such as MKB and PICCS in the authors' implementation, are only a trade-off and may not fully achieve the optimal temporal resolution. A predictable image quality as seen in conventional reconstruction methods, i.e., without total variation minimization, is a desirable property for reconstruction algorithms. Fast, iterative approaches such as the MKB can therefore be seen as a suitable tradeoff.

Temporal-correlated image reconstruction, also known as 4D CT image reconstruction, is a big challenge in computed tomography. The reasons for incorporating the temporal domain into the reconstruction are motions of the scanned object, which would otherwise lead to motion artifacts. The standard method for 4D CT image reconstruction is extracting single motion phases and reconstructing them separately. These reconstructions can suffer from undersampling artifacts due to the low number of used projections in each phase. There are different iterative methods which try to incorporate some a priori knowledge to compensate for these artifacts. In this paper we want to follow this strategy. The cost function we use is a higher dimensional cost function which accounts for the sparseness of the measured signal in the spatial and temporal directions. This leads to the definition of a higher dimensional total variation. The method is validated using in vivo cardiac micro-CT mouse data. Additionally, we compare the results to phase-correlated reconstructions using the FDK algorithm and a total variation constrained reconstruction, where the total variation term is only defined in the spatial domain. The reconstructed datasets show strong improvements in terms of artifact reduction and low-contrast resolution compared to other methods. Thereby the temporal resolution of the reconstructed signal is not affected.

Objective Artifacts from dental hardware affect image quality and the visualization of lesions in the oral cavity and oropharynx in computed tomography (CT). Therefore, magnetic resonance imaging is considered the imaging modality of choice in this region. Standard methods for metal artifact reduction (MAR) in CT replace the metal-affected raw data by interpolation, which is prone to new artifacts. We developed a generalized normalization technique for MAR (NMAR) that aims to suppress algorithm-induced artifacts and validated the performance of this algorithm in a clinical trial. Material and Methods A 3-dimensional forward projection identifies the metal-affected raw data in the original projections after metal is segmented in the image domain by thresholding. A prior image is used to normalize the projections before interpolation. The original raw data are divided pixel-wise by the projection data of the prior image and, after interpolation, are denormalized again. Data from 19 consecutive patients with metal artifacts from dental hardware were reconstructed with standard filtered backprojection (FBP), linear interpolation MAR (LIMAR), and NMAR. The image quality of slices containing metal was analyzed for the severity of artifacts and diagnostic value; magnetic resonance imaging performed the same day on a 3-T system served as a reference standard in all cases. Results A total of 260 slices containing metal dental hardware were analyzed. A total of 164 slices were nondiagnostic with FBP, 157 slices with LIMAR, and 87 slices with NMAR. The mean (SD) number of slices per patient with severe artifacts was 10.1 (3.7), 9.6 (4.6), and 5.4 (3.6) and the mean (SD) number of slices with artifacts affecting diagnostic confidence was 3.3 (1.7), 4.9 (2.9), and 3.7 (1.9) for FBP, LIMAR, and NMAR, respectively (P < 0.001). Pairwise comparison did not show significant differences between FBP and LIMAR (P = 0.40), but there were significant differences between FBP and NMAR as well as LIMAR and NMAR (both P < 0.001). Interobserver agreement was excellent (κ = 0.974). Two malignant lesions were unmasked with NMAR image reconstructions. No algorithm-related artifacts were detected in regions that did not contain metal in NMAR images. Conclusion Normalized MAR has the potential to improve image quality in patients with artifacts from dental hardware and to improve the diagnostic accuracy of CT of the oral cavity and oropharynx.

Dual energy CT (DECT) has proven its potential in oncological imaging. Considering the repeated follow-up examinations, radiation dose should not exceed conventional single energy CT (SECT). Comparison studies on the same scanner with a large number of patients, considering patient geometries and image quality, and exploiting full potential of SECT dose reduction are rare. Purpose of this retrospective study was to compare dose of dual source DECT versus dose-optimized SECT abdominal imaging in clinical routine.One hundred patients (62y (±14)) had either contrast-enhanced SECT including automatic voltage control (44) or DECT (56). CT dose index (CTDIvol), size-specific dose-estimate (SSDE) and dose-length product (DLP) were reported. Image noise (SD) was recorded as mean of three ROIs placed in subcutaneous fat and normalized to dose by [Formula: see text] . For dose-normalized contrast-to-noise ratio (CNRD), mean attenuation of psoas muscle (CTmuscle) and subcutaneous fat (CTfat) were compared by CNRD = (CTmuscle - CTfat)/SDn. Statistical significance was tested with two-sided t-test (α = 0.05).There was no significant difference (p < 0.05) between DECT and SECT: Mean CTDIvol was 14.2 mGy (±3.9) (DECT) and 14.3 mGy (±4.5) (SECT). Mean DLP was 680 mGy*cm (±220) (DECT) and 665 mGy*cm (±231) (SECT). Mean SSDE was 15.7 mGy (±1.9) (DECT) and 16.1 mGy (±2.5) (SECT). Mean SDn was 42.2 (±13.9) HU [Formula: see text] (DECT) and 47.8 (±14.9) HU [Formula: see text] (SECT). Mean CNRD was 3.9 (±1.3) [Formula: see text]. (DECT) and 4.0 (±1.3) [Formula: see text] (SECT).Abdominal DECT is feasible without increasing radiation dose or deteriorating image quality, even compared to dose-optimized SECT including automatic voltage control. Thus DECT can contribute to sophisticated oncological imaging without dose penalty.

Objectives Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) targeting the prostate-specific membrane antigen (PSMA) with a 68Ga-labelled PSMA-analog (68Ga-PSMA-11) is discussed as a promising diagnostic method for patients with suspicion or history of prostate cancer. One potential drawback of this method are severe photopenic (halo-) artifacts surrounding the bladder and the kidneys in the scatter-corrected PET images, which have been reported to occur frequently in clinical practice. The goal of this work was to investigate the occurrence and impact of these artifacts and, secondly, to evaluate variants of the standard scatter correction method with regard to halo-artifact suppression. Methods Experiments using a dedicated pelvis phantom were conducted to investigate whether the halo-artifact is modality-, tracer-, and/or concentration-dependent. Furthermore, 31 patients with history of prostate cancer were selected from an ongoing 68Ga-PSMA-11-PET/MRI study. For each patient, PET raw data were reconstructed employing six different variants of PET scatter correction: absolute scatter scaling, relative scatter scaling, and relative scatter scaling combined with prompt gamma correction, each of which was combined with a maximum scatter fraction (MaxSF) of MaxSF = 75% or MaxSF = 40%. Evaluation of the reconstructed images with regard to halo-artifact suppression was performed both quantitatively using statistical analysis and qualitatively by two independent readers. Results The phantom experiments did not reveal any modality-dependency (PET/MRI vs. PET/CT) or tracer-dependency (68Ga vs. 18F-FDG). Patient- and phantom-based data indicated that halo-artifacts derive from high organ-to-background activity ratios (OBR) between bladder/kidneys and surrounding soft tissue, with a positive correlation between OBR and halo size. Comparing different variants of scatter correction, reducing the maximum scatter fraction from the default value MaxSF = 75% to MaxSF = 40% was found to efficiently suppress halo-artifacts in both phantom and patient data. In 1 of 31 patients, reducing the maximum scatter fraction provided new PET-based information changing the patient’s diagnosis. Conclusion Halo-artifacts are particularly observed for 68Ga-PSMA-11-PET/MRI due to 1) the biodistribution of the PSMA-11-tracer resulting in large OBRs for bladder and kidneys and 2) inaccurate scatter correction methods currently used in clinical routine, which tend to overestimate the scatter contribution. If not compensated for, 68Ga-PSMA-11 uptake pathologies may be masked by halo-artifacts leading to false-negative diagnoses. Reducing the maximum scatter fraction was found to efficiently suppress halo-artifacts.

Purpose Dual‐energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissues compared to conventional single‐energy CT (SECT). Recent research shows that automatic multi‐organ segmentation of DECT data can improve DECT clinical applications. However, most segmentation methods are designed for SECT, while DECT has been significantly less pronounced in research. Therefore, a novel approach is required that is able to take full advantage of the extra information provided by DECT. Methods In the scope of this work, we proposed four three‐dimensional (3D) fully convolutional neural network algorithms for the automatic segmentation of DECT data. We incorporated the extra energy information differently and embedded the fusion of information in each of the network architectures. Results Quantitative evaluation using 45 thorax/abdomen DECT datasets acquired with a clinical dual‐source CT system was investigated. The segmentation of six thoracic and abdominal organs (left and right lungs, liver, spleen, and left and right kidneys) were evaluated using a fivefold cross‐validation strategy. In all of the tests, we achieved the best average Dice coefficients of 98% for the right lung, 98% for the left lung, 96% for the liver, 92% for the spleen, 95% for the right kidney, 93% for the left kidney, respectively. The network architectures exploit dual‐energy spectra and outperform deep learning for SECT. Conclusions The results of the cross‐validation show that our methods are feasible and promising. Successful tests on special clinical cases reveal that our methods have high adaptability in the practical application.

Photon-counting (PC) detectors for clinical computed tomography (CT) may offer improved imaging capabilities compared to conventional energy-integrating (EI) detectors, e.g. superior spatial resolution and detective efficiency. We here investigate if PCCT can reduce the administered dose in examinations aimed at quantifying trabecular bone microstructure. Five human vertebral bodies were scanned three times in an abdomen phantom (QRM, Germany) using an experimental dual-source CT (Somatom CounT, Siemens Healthineers, Germany) housing an EI detector (0.60 mm pixel size at the iso-center) and a PC detector (0.25 mm pixel size). A tube voltage of 120 kV was used. Tube current-time product for EICT was 355 mAs (23.8 mGy CTDI32 cm). Dose-matched UHR-PCCT (UHRdm, 23.8 mGy) and noise-matched acquisitions (UHRnm, 10.5 mGy) were performed and reconstructed to a voxel size of 0.156 mm using a sharp kernel. Measurements of bone mineral density (BMD) and trabecular separation (Tb.Sp) and Tb.Sp percentiles reflecting the different scales of the trabecular interspacing were performed and compared to a gold-standard measurement using a peripheral CT device (XtremeCT, SCANCO Medical, Switzerland) with an isotropic voxel size of 0.082 mm and 6.6 mGy CTDI10 cm. The image noise was quantified and the relative error with respect to the gold-standard along with the agreement between CT protocols using Lin's concordance correlation coefficient (rCCC) were calculated. The Mean ± StdDev of the measured image noise levels in EICT was 109.6 ± 3.9 HU. UHRdm acquisitions (same dose as EICT) showed a significantly lower noise level of 78.6 ± 4.6 HU (p = 0.0122). UHRnm (44% dose of EICT) showed a noise level of 115.8 ± 3.7 HU, very similar to EICT at the same spatial resolution. For BMD the overall Mean ± StdDev for EI, UHRdm and UHRnm were 114.8 ± 28.6 mgHA/cm3, 121.6 ± 28.8 mgHA/cm3 and 121.5 ± 28.6 mgHA/cm3, respectively, compared to 123.1 ± 25.5 mgHA/cm3 for XtremeCT. For Tb.Sp these values were 1.86 ± 0.54 mm, 1.80 ± 0.56 mm and 1.84 ± 0.52 mm, respectively, compared to 1.66 ± 0.48 mm for XtremeCT. The ranking of the vertebrae with regard to Tb.Sp data was maintained throughout all Tb.Sp percentiles and among the CT protocols and the gold-standard. The agreement between protocols was very good for all comparisons: UHRnm vs. EICT (BMD rCCC = 0.97; Tb.Sp rCCC = 0.998), UHRnm vs. UHRdm (BMD rCCC = 0.998; Tb.Sp rCCC = 0.993) and UHRdm vs. EICT (BMD rCCC = 0.97; Tb.Sp rCCC = 0.991). Consequently, the relative RMS-errors from linear regressions against the gold-standard for EICT, UHRdm and UHRnm were very similar for BMD (7.1%, 5.2% and 5.4%) and for Tb.Sp (3.3%, 3.3% and 2.9%), with a much lower radiation dose for UHRnm. Short-term reproducibility for BMD measurements was similar and below 0.2% for all protocols, but for Tb.Sp showed better results for UHR (about 1/3 of the level for EICT). In conclusion, CT with UHR-PC detectors demonstrated lower image noise and better reproducibility for assessments of bone microstructure at similar dose levels. For UHRnm, radiation exposure levels could be reduced by 56% without deterioration of performance levels in the assessment of bone mineral density and bone microstructure.

Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial and soft tissue resolution, as well as dose reduction have been achieved. Tube current modulation, automated exposure control, anatomy-based tube voltage (kV) selection, advanced x-ray beam filtration, and iterative image reconstruction techniques improved image quality and decreased radiation exposure. Cardiac imaging triggered the demand for high temporal resolution, volume acquisition, and high pitch modes with electrocardiogram synchronization. Plaque imaging in cardiac CT as well as lung and bone imaging demand for high spatial resolution. Today, we see a transition of photon-counting detectors from experimental and research prototype setups into commercially available systems integrated in patient care. Moreover, with respect to CT technology and CT image formation, artificial intelligence is increasingly used in patient positioning, protocol adjustment, and image reconstruction, but also in image preprocessing or postprocessing. The aim of this article is to give an overview of the technical specifications of up-to-date available whole-body and dedicated CT systems, as well as hardware and software innovations for CT systems in the near future.

To compare the image quality of electron beam tomography (EBT) and multislice spiral CT (MSCT) for coronary artery visualization.Two groups of 30 patients without coronary stenoses were studied by MSCT (4 x 1 mm collimation) or EBT (3 mm slice thickness). Contrast-to-noise ratio (CNR), overall length of the visualized arteries and vessel length free of motion artifacts were measured.Length of visualized arteries was equal in MSCT and EBT. In EBT, longer segments were depicted free of motion artifacts (MSCT: 73%, EBT: 92% of visualized length, P< 0.001) and CNR was significantly higher than in MSCT (15.4 vs. 9.0; P< 0.001). In both modalities, vessel diameters correlated closely to quantitative coronary angiography.EBT and MSCT permit reliable coronary artery visualization and measurement of vessel diameters. For the used scan protocol, MSCT images had a lower CNR and were more frequently affected by motion.

The new spiral multislice computed tomography (CT) scanners and the significant increase in rotation speed offer great potential for cardiac imaging with X-ray CT. We have therefore developed the dedicated cardiac reconstruction algorithms 180 degrees multislice cardio interpolation (MCI) and 180 degrees multislice cardio delta (MCD) and here offer further details and validation. The algorithm 180 degreesMCI is an electrocardiogram (ECG)-correlated filtering (or weighting) algorithm in both the cardiac phase and in the z-position. Effective scan times (absolute temporal resolution) of as low as t(eff) = 56 ms are possible, assuming M 4 simultaneously measured slices at a rotation time of t(rot) = 0.5 s and S < or = d < or = 3S for the table feed d per rotation, where S denotes the collimated slice thickness. The relative temporal resolution w (fraction of the heart cycle depicted in the image), which is the more important parameter in cardiac imaging, will then be as low as w = 12.5% of the heart cycle. The second approach, 180 degreesMCD, is an ECG-correlated partial scan reconstruction of 180 degrees + delta data with delta << phi (fan-angle). Its absolute temporal resolution lies in the order of 250 ms (for the central ray, i.e., for the center of rotation), and the relative temporal resolution w increases with increasing heart rate, e.g., from typically w = 25% at fH = 60 min(-1) to w = 50% at fH = 120 min(-1), assuming again t(rot) = 0.5 s. For validation purposes, we have done simulations of a virtual cardiac motion phantom, measurements of a dedicated cardiac calibration and motion phantom, and we have reconstructed patient data with simultaneously acquired ECG. Both algorithms significantly improve the image quality compared with the standard reconstruction algorithms 180 degrees multislice linear interpolation (MLI) and 180 degrees multislice filtered interpolation (MFI). However, 180 degreesMCI is clearly superior to 180 degreesMCD for all heart rates. This is best illustrated by multiplanar reformations (MPR) or other three-dimensional (3-D) displays of the volume. 180 degreesMCI, due to its higher temporal resolution, is best for spatial and temporal four-dimensional (4-D) tracking of the anatomy. A tunable scanner rotation time to avoid resonance behavior of the heart rate and the scanner's rotation and shorter rotation times would be of further benefit.

Subsecond single-slice, multi-slice or cone-beam spiral computed tomography (SSCT, MSCT, CBCT) offer great potential for improving heart imaging. Together with the newly developed phase-correlated cardiac reconstruction algorithms 180 degrees MCD and 180 degrees MCI [Med. Phys. 27, 1881-1902 (2000)] or related algorithms provided by the CT manufacturers, high image quality can be achieved. These algorithms require information about the cardiac motion, i.e., typically the simultaneously recorded electrocardiogram (ECG), to synchronize the reconstruction with the cardiac motion. Neither data acquired without ECG information (standard patients) nor acquisitions with corrupted ECG information can be handled adequately. We developed a method to extract the appropriate information about cardiac motion directly from the measured raw data (projection data). The so-called kymogram function is a measure of the cardiac motion as a function of time t or as a function of the projection angle alpha. In contrast to the ECG which is a global measure of the heart's electric excitation, the kymogram is a local measure of the heart motion at the z-position z(a) at projection angle a. The patient's local heart rate as well as the necessary synchronization information to be used with phase-correlated algorithms can be extracted from the kymogram by using a series of signal processing steps. The kymogram information is shown to be adequate to substitute the ECG information. Computer simulations with simulated ECG and patient measurements with simultaneously acquired ECG were carried out for a multislice scanner providing M = 4 slices to evaluate these new approaches. Both the ECG function and the kymogram function were used for reconstruction. Both were highly correlated regarding the periodicity information used for reconstruction. In 21 out of 25 consecutive cases the kymogram approach was equivalent to the ECG-correlated reconstruction; only minor differences in image quality between both methods were observed. For one patient the synchronization information detected by the ECG monitor turned out to be wrong; here, the kymogram constituted the only approach that provided useful reconstructions. Patient studies with 12 and 16 slices indicate the usefulness of our approach for cone-beam CT scans. Kymogram-correlated reconstructions also appear to have the potential to improve imaging of pericardial lung areas in general.

Purpose: In image‐guided radiation therapy (IGRT) valuable information for patient positioning, dose verification, and adaptive treatment planning is provided by an additional kV imaging unit. However, due to the limited gantry rotation speed during treatment the typical acquisition time is quite long. Tomographic images of the thorax suffer from motion blurring or, if a gated 4D reconstruction is performed, from significant streak artifacts. Our purpose is to provide a method that reliably estimates respiratory motion in presence of severe artifacts. The estimated motion vector fields are then used for motion‐compensated image reconstruction to provide high quality respiratory‐correlated 4D volumes for on‐board cone‐beam CT (CBCT) scans. Methods: The proposed motion estimation method consists of a model that explicitly addresses image artifacts because in presence of severe artifacts state‐of‐the‐art registration methods tend to register artifacts rather than anatomy. Our artifact model, e.g., generates streak artifacts very similar to those included in the gated 4D CBCT images, but it does not include respiratory motion. In combination with a registration strategy, the model gives an error estimate that is used to compensate the corresponding errors of the motion vector fields that are estimated from the gated 4D CBCT images. The algorithm is tested in combination with a cyclic registration approach using temporal constraints and with a standard 3D–3D registration approach. A qualitative and quantitative evaluation of the motion‐compensated results was performed using simulated rawdata created on basis of clinical CT data. Further evaluation includes patient data which were scanned with an on‐board CBCT system. Results: The model‐based motion estimation method is nearly insensitive to image artifacts of gated 4D reconstructions as they are caused by angular undersampling. The motion is accurately estimated and our motion‐compensated image reconstruction algorithm can correct for it. Motion artifacts of 3D standard reconstruction are significantly reduced, while almost no new artifacts are introduced. Conclusions: Using the artifact model allows to accurately estimate and compensate for patient motion, even if the initial reconstructions are of very low image quality. Using our approach together with a cyclic registration algorithm yields a combination which shows almost no sensitivity to sparse‐view artifacts and thus ensures both high spatial and high temporal resolution.

Abstract Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non-invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and, then point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by advances in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumour phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi-dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging.

Purpose To assess the dose-normalized iodine contrast-to-noise-ratio (CNRD) improvement and contrast media reduction potential obtained with photon-counting (PC) CT compared to conventional energy-integrating (EI) CT as a function of patient size and tube voltage. Method Images of a semi-anthropomorphic phantom of different sizes (small, medium, large) equipped with vials containing different iodine concentrations were acquired at the SOMATOM CounT prototype CT system using tube voltages of 80 kV–140 kV. CNRD is evaluated in reconstructions obtained using the EI detector, the PC detector using a single bin, and in reconstructions obtained by statistically optimally weighting acquisitions with two bins. Iodine CNRD improvements, potential dose reduction and the potential contrast media volume reduction are reported. Results In general, iodine CNRD improvement increases with increasing tube voltage for all patient sizes. In particular, if only one energy bin is used, the CNRD improvement is up to 30 % (small: 10 %, medium: 18 %, large: 30 %) and up to 37 % if an optimal weighting of two bins is performed (small: 13 %, medium: 25 %, large: 37 %) which is equivalent to the potential contrast media volume reduction. The improved iodine CNRD of PC compared to EI may allow for a potential radiation dose reduction of up to 46 %. Conclusions All patients’ iodine contrast at given x-ray dose, and particularly medium and large sized patients acquired at higher tube voltages, may benefit from photon-counting CT. The iodine contrast improvement can be used to reduce patient dose or to reduce the amount of contrast agent that is administered.

Conventional CT scanners use energy-integrating detectors (EIDs). Photon-counting detector (PCD) computed tomography (CT) utilizes a CT detector technology based on smaller detector pixels capable of counting single photons and in addition discriminating their energy. Goal of this study was to explore the potential of higher spatial resolution for imaging of bone metastases. Four female patients with histologically confirmed breast cancer and bone metastases were included between July and October 2019. All patients underwent conventional EID CT scans followed by a high resolution non-contrast experimental PCD CT scan. Ultra-high resolution (UHR) reconstruction kernels were used to reconstruct axial slices with voxel sizes of 0.3 mm × 0.3 mm (inplane) × 1 mm (z-direction). Four radiologists blinded for patient identity assessed the images and compared the quality to conventional CT using a qualitative Likert scale. In this case series, we present images of bone metastases in breast cancer patients using an experimental PCD CT scanner and ultra-high-resolution kernels. A tendency to both a smaller inter-reader variability in the structural assessment of lesion sizes and in the readers' opinion to an improved visualization of lesion margins and content was observed. In conclusion, while further studies are warranted, PCD CT has a high potential for therapy monitoring in breast cancer.

With the rising number of computed tomography (CT) examinations and the trend toward personalized medicine, patient-specific dose estimates are becoming more and more important in CT imaging. However, current approaches are often too slow or too inaccurate to be applied routinely. Therefore, we propose the so-called deep dose estimation (DDE) to provide highly accurate patient dose distributions in real time METHODS: To combine accuracy and computational performance, the DDE algorithm uses a deep convolutional neural network to predict patient dose distributions. To do so, a U-net like architecture is trained to reproduce Monte Carlo simulations from a two-channel input consisting of a CT reconstruction and a first-order dose estimate. Here, the corresponding training data were generated using CT simulations based on 45 whole-body patient scans. For each patient, simulations were performed for different anatomies (pelvis, abdomen, thorax, head), different tube voltages (80 kV, 100 kV, 120 kV), different scan trajectories (circle, spiral), and with and without bowtie filtration and tube current modulation. Similar simulations were performed using a second set of eight whole-body CT scans from the Visual Concept Extraction Challenge in Radiology (Visceral) project to generate testing data. Finally, the DDE algorithm was evaluated with respect to the generalization to different scan parameters and the accuracy of organ dose and effective dose estimates based on an external organ segmentation.DDE dose distributions were quantified in terms of the mean absolute percentage error (MAPE) and a gamma analysis with respect to the ground truth Monte Carlo simulation. Both measures indicate that DDE generalizes well to different scan parameters and different anatomical regions with a maximum MAPE of 6.3% and a minimum gamma passing rate of 91%. Evaluating the organ dose values for all organs listed in the International Commission on Radiological Protection (ICRP) recommendation, shows an average error of 3.1% and maximum error of 7.2% (bone surface).The DDE algorithm provides an efficient approach to determine highly accurate dose distributions. Being able to process a whole-body CT scan in about 1.5 s, it provides a valuable alternative to Monte Carlo simulations on a graphics processing unit (GPU). Here, the main advantage of DDE is that it can be used on top of any existing Monte Carlo code such that real-time performance can be achieved without major adjustments. Thus, DDE opens up new options not only for dosimetry but also for scan and protocol optimization.

To investigate the image quality of a low-dose dental imaging protocol in the first clinical photon-counting computed tomography (PCCT) system in comparison to a normal-dose acquisition in a digital volume tomography (DVT) system. Clinical PCCT systems offer an increased spatial resolution compared to previous generations of clinical systems. Their spatial resolution is in the order of dental DVT systems. Resolution-matched acquisitions of ten porcine jaws were performed in a PCCT (Naeotom Alpha, Siemens Healthineers) and in a DVT (Orthophos XL, Dentsply Sirona). PCCT images were acquired with 90 kV at a dose of 1 mGy CTDI16cm. DVT used 85 kV at 4 mGy. Image reconstruction was performed using the standard algorithms of each system to a voxel size of 160 × 160 × 200 µm. The dose-normalized contrast-to-noise ratio (CNRD) was measured between dentine and enamel and dentine and bone. Two readers evaluated overall diagnostic quality of images and quality of relevant structures such as root channels and dentine. CNRD is higher in all PCCT acquisitions. CNRD is 37% higher for the contrast dentine-enamel and 31% higher for the dentine-bone contrast (p<0.05). Overall diagnostic image quality was higher for PCCT over DVT (p<0.02 and p<0.04 for readers 1 and 2). Quality scores for anatomical structures were higher in PCCT compared to DVT (all p<0.05). Inter- and intrareader reproducibility were acceptable (all ICC>0.64). PCCT provides an increased image quality over DVT even at a lower dose level and might enable complex dental imaging protocols in the future. The evolution of photon-counting technology and it's optimization will increasingly move dental imaging towards standardized 3D visualizations providing both minimal radiation exposure and high diagnostic accuracy.

We have developed a new approximate Feldkamp‐type algorithm that we call the extended parallel backprojection (EPBP). Its main features are a phase‐weighted backprojection and a voxel‐by‐voxel 180° normalization. The first feature ensures three‐dimensional (3‐D) and 4‐D capabilities with one and the same algorithm; the second ensures 100% detector usage (each ray is accounted for). The algorithm was evaluated using simulated data of a thorax phantom and a cardiac motion phantom for scanners with up to 256 slices. Axial (circle and sequence) and spiral scan trajectories were investigated. The standard reconstructions (EPBPStd) are of high quality, even for as many as 256 slices. The cardiac reconstructions (EPBPCI) are of high quality as well and show no significant deterioration of objects even far off the center of rotation. Since EPBPCI uses the cardio interpolation (CI) phase weighting the temporal resolution is equivalent to that of the well‐established single‐slice and multislice cardiac approaches 180°CI, 180°MCI, and ASSRCI, respectively, and lies in the order of 50 to 100 ms for rotation times between 0.4 and 0.5 s. EPBP appears to fulfill all required demands. Especially the phase‐correlated EPBP reconstruction of cardiac multiple circle scan data is of high interest, e.g., for dynamic perfusion studies of the heart.

In the beginning of 2004 medical spiral-CT scanners that acquire up to 64 slices simultaneously became available. Most manufacturers use a straightforward acquisition principle, namely an x-ray focus rotating on a circular path and an opposing cylindrical detector whose rotational center coincides with the x-ray focus. The 64-slice scanner available to us, a Somatom Sensation 64 spiral cone-beam CT scanner (Siemens, Medical Solutions, Forchheim, Germany), makes use of a flying focal spot (FFS) that allows for view-by-view deflections of the focal spot in the rotation direction (/spl alpha/FFS) and in the z-direction (zFFS) with the goal of reducing aliasing artifacts. The FFS feature doubles the sampling density in the radial direction (channel direction, /spl alpha/FFS) and in the longitudinal direction (detector row direction or z-direction, zFFS). The cost of increased radial and azimuthal sampling is a two- or four-fold reduction of azimuthal sampling (angular sampling). To compensate for the potential reduction of azimuthal sampling the scanner simply increases the number of detector read-outs (readings) per rotation by a factor two or four. Then, up to four detector readings contribute to what we define as one view or one projection. A significant reduction of in-plane aliasing and of aliasing in the z-direction can be expected. Especially the latter is of importance to spiral CT scans where aliasing is known to produce so-called windmill artifacts. We have derived and analyzed the optimal focal spot deflection values /spl part//spl alpha/ and /spl part/z as they would ideally occur in our scanner. Based upon these we show how image reconstruction can be performed in general. A simulation study showing reconstructions of mathematical phantoms further provides evidence that image quality can be significantly improved with the FFS. Aliasing artifacts, that manifest as streaks emerging from high-contrast objects, and windmill artifacts are reduced by almost an order of magnitude with the FFS compared to a simulation without FFS. Patient images acquired with our 64-slice cone-beam CT scanner support these results.

Purpose: Kilovoltage cone‐beam computed tomography (CBCT) is widely used in image‐guided radiation therapy for exact patient positioning prior to the treatment. However, producing time series of volumetric images (4D CBCT) of moving anatomical structures remains challenging. The presented work introduces a novel method, combining high temporal resolution inside anatomical regions with strong motion and image quality improvement in regions with little motion. Methods: In the proposed method, the projections are divided into regions that are subject to motion and regions at rest. The latter ones will be shared among phase bins, leading thus to an overall reduction in artifacts and noise. An algorithm based on the concept of optical flow was developed to analyze motion‐induced changes between projections. The technique was optimized to distinguish patient motion and motion deriving from gantry rotation. The effectiveness of the method is shown in numerical simulations and patient data. Results: The images reconstructed from the presented method yield an almost the same temporal resolution in the moving volume segments as a conventional phase‐correlated reconstruction, while reducing the noise in the motionless regions down to the level of a standard reconstruction without phase correlation. The proposed simple motion segmentation scheme is yet limited to rotation speeds of less than . Conclusions: The method reduces the noise in the reconstruction and increases the image quality. More data are introduced for each phase‐correlated reconstruction, and therefore the applied dose is used more efficiently.

Purpose: In image‐guided radiation therapy an additional kV imaging system next to the linear particle accelerator provides information for an accurate patient positioning. However, the acquisition time of the system is much longer than the patientˈs breathing cycle due to the low gantry rotation speed. Our purpose is a cyclic registration in the context of motion‐compensated image reconstruction that provides high quality respiratory‐correlated 4D volumes for on‐board flat panel detector cone‐beam CT scans. Methods: Based on the small motion assumption, widely used within registration algorithms, a strategy is developed for motion estimation. In this strategy temporal restrictions are incorporated, for example, the cyclic motion patterns of respiration. The resultant cyclic registration method is to show less sensitivity on image artifacts, in particular on artifacts due to projection data sparsification. Using a new cyclic registration method a motion estimation is performed on respiratory‐correlated reconstructions, and the obtained motion vector fields are used for motion compensation. Results: The proposed cyclic registration is evaluated in the context of motion‐compensated image reconstruction using simulated data and patient data. Motion artifacts of 3D standard reconstructions can be significantly reduced by the resulting cyclic motion compensation. The method outperforms the respiratory‐correlated reconstructions regarding sparse‐view artifacts and maintains the high temporal resolution at the same time. Image artifacts show only minor to almost no effect on the motion estimation using the cyclic registration. Conclusions: The cyclic motion compensation approach provides respiratory‐correlated volumes with high image quality. The cyclic motion estimation is of such low sensitivity to sparse‐view artifacts, that it is capable to determine high quality motion vector fields based on reconstructions of low sampled data.

The purpose of this study was to develop and evaluate the hybrid scatter correction algorithm (HSC) for CT imaging. Therefore, two established ways to perform scatter correction, i.e. physical scatter correction based on Monte Carlo simulations and a convolution-based scatter correction algorithm, were combined in order to perform an object-dependent, fast and accurate scatter correction. Based on a reconstructed CT volume, patient-specific scatter intensity is estimated by a coarse Monte Carlo simulation that uses a reduced amount of simulated photons in order to reduce the simulation time. To further speed up the Monte Carlo scatter estimation, scatter intensities are simulated only for a fraction of all projections. In a second step, the high noise estimate of the scatter intensity is used to calibrate the open parameters in a convolution-based algorithm which is then used to correct measured intensities for scatter. Furthermore, the scatter-corrected intensities are used in order to reconstruct a scatter-corrected CT volume data set. To evaluate the scatter reduction potential of HSC, we conducted simulations in a clinical CT geometry and measurements with a flat detector CT system. In the simulation study, HSC-corrected images were compared to scatter-free reference images. For the measurements, no scatter-free reference image was available. Therefore, we used an image corrected with a low-noise Monte Carlo simulation as a reference. The results show that the HSC can significantly reduce scatter artifacts. Compared to the reference images, the error due to scatter artifacts decreased from 100% for uncorrected images to a value below 20% for HSC-corrected images for both the clinical (simulated data) and the flat detector CT geometry (measurement). Compared to a low-noise Monte Carlo simulation, with the HSC the number of photon histories can be reduced by about a factor of 100 per projection without losing correction accuracy. Furthermore, it was sufficient to calibrate the parameters in the convolution model at an angular increment of about 20°. The reduction of the simulated photon histories together with the reduced amount of simulated Monte Carlo scatter projections decreased the total runtime of the scatter correction by about two orders of magnitude for the cases investigated here when using the HSC instead of a low-noise Monte Carlo simulation for scatter correction.

The contribution of scattered x-rays to the acquired projection data is a severe issue in cone-beam CT (CBCT). Due to the large cone angle, scatter-to-primary ratios may easily be in the order of 1. The corresponding artifacts which appear as cupping or dark streaks in the CT reconstruction may impair the diagnostic value of the CT examination. Therefore, appropriate scatter correction is essential. The gold standard is to use a Monte Carlo photon transport code to predict the distribution of scattered x-rays which can be subtracted from the measurement subsequently. However, long processing times of Monte Carlo simulations prohibit them to be used routinely. To enable fast and accurate scatter estimation we propose the deep scatter estimation (DSE). It uses a deep convolutional neural network which is trained to reproduce the output of Monte Carlo simulations using only the acquired projection data as input. Once the network is trained, DSE performs in real-time. In the present study we demonstrate the feasibility of DSE using simulations of CBCT head scans at different tube voltages. The performance is tested on data sets that significantly differ from the training data. Thereby, the scatter estimates deviate less than 2% from the Monte Carlo ground truth. A comparison to kernel-based scatter estimation techniques, as they are used today, clearly shows superior performance of DSE while being similar in terms of processing time.

We propose a new convolutional neural network architecture for image reconstruction in sparse view computed tomography. The proposed network consists of a cascade of U-nets and data consistency layers. While the U-nets address the undersampling artifacts, the data consistency layers model the specific scanner geometry and make direct use of measured data. We train the network cascade end-to-end on sparse view cardiac CT images. The proposed network’s performance is evaluated according to different quantitative measures and compared to the one of a cascade with fully convolutional neural networks with residual connections and to the one of a single U-net with approximately the same number of trainable parameters. While in both experiments the methods show similar performance in terms of quantitative measures, our proposed U-nets cascade yields superior visual results and better preserves the overall image structure as well as fine diagnostic details, e.g. the coronary arteries. The latter is also confirmed by a statistically significant increase of the Haar-wavelet-based perceptual similarity index measure in all the experiments.

In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks (NNs) and cascaded NNs have been reported to achieve state-of-the-art results with respect to various quantitative quality measures as PSNR, NRMSE and SSIM across different imaging modalities. However, the fact that these approaches employ the application of the forward and adjoint operators repeatedly in the network architecture requires the network to process the whole images or volumes at once, which for some applications is computationally infeasible. In this work, we follow a different reconstruction strategy by strictly separating the application of the NN, the regularization of the solution and the consistency with the measured data. The regularization is given in the form of an image prior obtained by the output of a previously trained NN which is used in a Tikhonov regularization framework. By doing so, more complex and sophisticated network architectures can be used for the removal of the artefacts or noise than it is usually the case in iterative NNs. Due to the large scale of the considered problems and the resulting computational complexity of the employed networks, the priors are obtained by processing the images or volumes as patches or slices. We evaluated the method for the cases of 3D cone-beam low dose CT and undersampled 2D radial cine MRI and compared it to a total variation-minimization-based reconstruction algorithm as well as to a method with regularization based on learned overcomplete dictionaries. The proposed method outperformed all the reported methods with respect to all chosen quantitative measures and further accelerates the regularization step in the reconstruction by several orders of magnitude.

Purpose During a typical cardiac short scan, the heart can move several millimeters. As a result, the corresponding CT reconstructions may be corrupted by motion artifacts. Especially the assessment of small structures, such as the coronary arteries, is potentially impaired by the presence of these artifacts. In order to estimate and compensate for coronary artery motion, this manuscript proposes the deep partial angle‐based motion compensation (Deep PAMoCo). Methods The basic principle of the Deep PAMoCo relies on the concept of partial angle reconstructions (PARs), that is, it divides the short scan data into several consecutive angular segments and reconstructs them separately. Subsequently, the PARs are deformed according to a motion vector field (MVF) such that they represent the same motion state and summed up to obtain the final motion‐compensated reconstruction. However, in contrast to prior work that is based on the same principle, the Deep PAMoCo estimates and applies the MVF via a deep neural network to increase the computational performance as well as the quality of the motion compensated reconstructions. Results Using simulated data, it could be demonstrated that the Deep PAMoCo is able to remove almost all motion artifacts independent of the contrast, the radius and the motion amplitude of the coronary artery. In any case, the average error of the CT values along the coronary artery is about 25 HU while errors of up to 300 HU can be observed if no correction is applied. Similar results were obtained for clinical cardiac CT scans where the Deep PAMoCo clearly outperforms state‐of‐the‐art coronary artery motion compensation approaches in terms of processing time as well as accuracy. Conclusions The Deep PAMoCo provides an efficient approach to increase the diagnostic value of cardiac CT scans even if they are highly corrupted by motion.

Purpose Metal artifacts can drastically reduce the diagnostic value of computed tomography (CT) images. Even the state‐of‐the‐art algorithms cannot remove them completely. Photon‐counting CT inherently provides spectral information, similar to dual‐energy CT. Many applications, such as material decomposition, are not possible when metal artifacts are present. Our aim is to develop a prior‐based metal artifact reduction specifically for photon‐counting CT that can correct each bin image individually or in their combinations. Methods Photon‐counting CT sorts incoming photons into several energy bins, producing bin and threshold images containing spectral information. We use this spectral information to obtain a better prior image for the state‐of‐the‐art metal artifact reduction algorithm FSNMAR. First, we apply a non‐linear transformation to the bin images to obtain bone‐emphasized images. Subsequently, we forward‐project the bin images and bone‐emphasized images and multiply the resulting sinograms with each other element‐wise to mimic beam hardening effects. These sinograms are reconstructed and linearly combined to produce an artifact‐reduced image. The coefficients of this linear combination are automatically determined by minimizing a threshold‐based cost function in the image domain. After thresholding, we obtain the prior image for FSNMAR, which is applied to the individual bin images and the lowest threshold image. We test our photon‐counting normalized metal artifact reduction (PCNMAR) on forensic CT data and compare it to conventional FSNMAR, where the prior is generated via linear sinogram inpainting. For numerical analysis, we compute both the standard deviation in an ROI with metal artifacts and the CNR of soft tissue and fat. Results PCNMAR can effectively reduce metal artifacts without sacrificing the overall image quality. Compared to FSNMAR, our method produces fewer secondary artifacts and is more consistent with the measurements. Areas that contain metal, air, and soft tissue are more accurate in PCNMAR. In some cases, the standard deviation in the artifact ROI is reduced by more than 50% relative to FSNMAR, while the CNR values are similar. If extreme artifacts are present, PCNMAR is unable to outperform FSNMAR. Using either two, four, or only the highest energy bin to produce the prior image yielded comparable results. Conclusions PCNMAR is an effective method of reducing metal artifacts in photon‐counting CT. The spectral information available in photon‐counting CT is highly beneficial for metal artifact reduction, especially the high‐energy bin, which inherently contains fewer artifacts. While scanning with four instead of two bins does not provide a better artifact reduction, it allows for more freedom in the selection of energy thresholds.

Micro-CT imaging of animal hearts typically requires a double gating procedure because scans during a breath-hold are not possible due to the long scan times and the high respiratory rates, Simultaneous respiratory and cardiac gating can either be done prospectively or retrospectively. True five-dimensional information can be either retrieved with retrospective gating or with prospective gating if several prospective gates are acquired. In any case, the amount of information available to reconstruct one volume for a given respiratory and cardiac phase is orders of magnitud lower than the total amount of information acquired. For example, the reconstruction of a volume from a 10% wide respiratory and a 20% wide cardiac window uses only 2% of the data acquired. Achieving a similar image quality as a nongated scan would therefore require to increase the amount of data and thereby the dose to the animal by up to a factor of 50.To achieve the goal of low-dose phase-correlated (LDPC) imaging, the authors propose to use a highly efficient combination of slightly modified existing algorithms. In particular, the authors developed a variant of the McKinnon-Bates image reconstruction algorithm and combined it with bilateral filtering in up to five dimensions to significantly reduce image noise without impairing spatial or temporal resolution.The preliminary results indicate that the proposed LDPC reconstruction method typically reduces image noise by a factor of up to 6 (e.g., from 170 to 30 HU), while the dose values lie in a range from 60 to 500 mGy. Compared to other publications that apply 250-1800 mGy for the same task [C. T. Badea et al., "4D micro-CT of the mouse heart," Mol. Imaging 4(2), 110-116 (2005); M. Drangova et al., "Fast retrospectively gated quantitative four-dimensional (4D) cardiac micro computed tomography imaging of free-breathing mice," Invest. Radiol. 42(2), 85-94 (2007); S. H. Bartling et al., "Retrospective motion gating in small animal CT of mice and rats," Invest. Radiol. 42(10), 704-714 (2007)], the authors' LDPC approach therefore achieves a more than tenfold dose usage improvement.The LDPC reconstruction method improves phase-correlated imaging from highly undersampled data. Artifacts caused by sparse angular sampling are removed and the image noise is decreased, while spatial and temporal resolution are preserved. Thus, the administered dose per animal can be decreased allowing for long-term studies with reduced metabolic inference.

Purpose: Image quality in computed tomography (CT) often suffers from artifacts which may reduce the diagnostic value of the image. In many cases, these artifacts result from missing or corrupt regions in the projection data, e.g., in the case of metal, truncation, and limited angle artifacts. The authors propose a generalized correction method for different kinds of artifacts resulting from missing or corrupt data by making use of available prior knowledge to perform data completion. Methods: The proposed prior‐based artifact correction (PBAC) method requires prior knowledge in form of a planning CT of the same patient or in form of a CT scan of a different patient showing the same body region. In both cases, the prior image is registered to the patient image using a deformable transformation. The registered prior is forward projected and data completion of the patient projections is performed using smooth sinogram inpainting. The obtained projection data are used to reconstruct the corrected image. Results: The authors investigate metal and truncation artifacts in patient data sets acquired with a clinical CT and limited angle artifacts in an anthropomorphic head phantom data set acquired with a gantry‐based flat detector CT device. In all cases, the corrected images obtained by PBAC are nearly artifact‐free. Compared to conventional correction methods, PBAC achieves better artifact suppression while preserving the patient‐specific anatomy at the same time. Further, the authors show that prominent anatomical details in the prior image seem to have only minor impact on the correction result. Conclusions: The results show that PBAC has the potential to effectively correct for metal, truncation, and limited angle artifacts if adequate prior data are available. Since the proposed method makes use of a generalized algorithm, PBAC may also be applicable to other artifacts resulting from missing or corrupt data.

Purpose: Scattered radiation is one of the major problems facing image quality in flat detector cone‐beam computed tomography (CBCT). Previously, a new scatter estimation and correction method using primary beam modulation has been proposed. The original image processing technique used a frequency‐domain‐based analysis, which proved to be sensitive to the accuracy of the modulator pattern both spatially and in amplitude as well as to the frequency of the modulation pattern. In addition, it cannot account for penumbra effects that occur, for example, due to the finite focal spot size and the scatter estimate can be degraded by high‐frequency components of the primary image. Methods: In this paper, the authors present a new way to estimate the scatter using primary modulation. It is less sensitive to modulator nonidealities and most importantly can handle arbitrary modulator shapes and changes in modulator attenuation. The main idea is that the scatter estimation can be expressed as an optimization problem, which yields a separation of the scatter and the primary image. The method is evaluated using simulated and experimental CBCT data. The scattering properties of the modulator itself are analyzed using a Monte Carlo simulation. Results: All reconstructions show strong improvements of image quality. To quantify the results, all images are compared to reference images (ideal simulations and collimated scans). Conclusions: The proposed modulator‐based scatter reduction algorithm may open the field of flat detector‐based imaging to become a quantitative modality. This may have significant impact on C‐arm imaging and on image‐guided radiation therapy.

The aim of this study was to develop and verify a method to obtain good temporal resolution T2-weighted 4-dimensional (4D-T2w) magnetic resonance imaging (MRI) by using motion information from T1-weighted 4D (4D-T1w) MRI, to support treatment planning in MR-guided radiotherapy.Ten patients with primary non-small cell lung cancer were scanned at 1.5 T axially with a volumetric T2-weighted turbo spin echo sequence gated to exhalation and a volumetric T1-weighted stack-of-stars spoiled gradient echo sequence with golden angle spacing acquired in free breathing. From the latter, 20 respiratory phases were reconstructed using the recently developed 4D joint MoCo-HDTV algorithm based on the self-gating signal obtained from the k-space center. Motion vector fields describing the respiratory cycle were obtained by deformable image registration between the respiratory phases and projected onto the T2-weighted image volume. The resulting 4D-T2w volumes were verified against the 4D-T1w volumes: an edge-detection method was used to measure the diaphragm positions; the locations of anatomical landmarks delineated by a radiation oncologist were compared and normalized mutual information was calculated to evaluate volumetric image similarity.High-resolution 4D-T2w MRI was obtained. Respiratory motion was preserved on calculated 4D-T2w MRI, with median diaphragm positions being consistent with less than 6.6 mm (2 voxels) for all patients and less than 3.3 mm (1 voxel) for 9 of 10 patients. Geometrical positions were coherent between 4D-T1w and 4D-T2w MRI as Euclidean distances between all corresponding anatomical landmarks agreed to within 7.6 mm (Euclidean distance of 2 voxels) and were below 3.8 mm (Euclidean distance of 1 voxel) for 355 of 470 pairs of anatomical landmarks. Volumetric image similarity was commensurate between 4D-T1w and 4D-T2w MRI, as mean percentage differences in normalized mutual information (calculated over all respiratory phases and patients), between corresponding respiratory phases of 4D-T1w and 4D-T2w MRI and the tie-phase of 4D-T1w and 3-dimensional T2w MRI, were consistent to 0.41% ± 0.37%. Four-dimensional T2w MRI displayed tumor extent, structure, and position more clearly than corresponding 4D-T1w MRI, especially when mobile tumor sites were adjacent to organs at risk.A methodology to obtain 4D-T2w MRI that retrospectively applies the motion information from 4D-T1w MRI to 3-dimensional T2w MRI was developed and verified. Four-dimensional T2w MRI can assist clinicians in delineating mobile lesions that are difficult to define on 4D-T1w MRI, because of poor tumor-tissue contrast.

Purpose: Cardiac in vivo micro‐CT imaging of small animals typically requires double gating due to long scan times and high respiratory rates. The simultaneous respiratory and cardiac gating can either be done prospectively or retrospectively. In any case, for true 5D imaging, i.e., three spatial dimensions plus one respiratory‐temporal dimension plus one cardiac temporal dimension, the amount of information corresponding to a given respiratory and cardiac phase is orders of magnitude lower than the total amount of information acquired. Achieving similar image quality for 5D than for usual 3D investigations would require increasing the amount of data and thus the applied dose to the animal. Therefore, the goal is phase‐correlated imaging with high image quality but without increasing the dose level. Methods: To achieve this, the authors propose a new image reconstruction algorithm that makes use of all available projection data, also of that corresponding to other motion windows. In particular, the authors apply a motion‐compensated image reconstruction approach that sequentially compensates for respiratory and cardiac motion to decrease the impact of sparsification. In that process, all projection data are used no matter which motion phase they were acquired in. Respiratory and cardiac motion are compensated for by using motion vector fields. These motion vector fields are estimated from initial phase‐correlated reconstructions based on a deformable registration approach. To decrease the sensitivity of the registration to sparse‐view artifacts, an artifact model‐based approach is used including a cyclic consistent nonrigid registration algorithm. Results: The preliminary results indicate that the authors’ approach removes the sparse‐view artifacts of conventional phase‐correlated reconstructions while maintaining temporal resolution. In addition, it achieves noise levels and spatial resolution comparable to that of nongated reconstructions due to the improved dose usage. By using the proposed motion estimation, no sensitivity to streaking artifacts has been observed. Conclusions: Using sequential double gating combined with artifact model‐based motion estimation allows to accurately estimate respiratory and cardiac motion from highly undersampled data. No sensitivity to streaking artifacts introduced by sparse angular sampling has been observed for the investigated dose levels. The motion‐compensated image reconstruction was able to correct for both, respiratory and cardiac motion, by applying the estimated motion vector fields. The administered dose per animal can thus be reduced for 5D imaging allowing for longitudinal studies at the highest image quality.

Purpose: The polychromatic nature of the x‐ray beams and their effects on the reconstructed image are often disregarded during standard image reconstruction. This leads to cupping and beam hardening artifacts inside the reconstructed volume. To correct for a general cupping, methods like water precorrection exist. They correct the hardening of the spectrum during the penetration of the measured object only for the major tissue class. In contrast, more complex artifacts like streaks between dense objects need other techniques of correction. If using only the information of one single energy scan, there are two types of corrections. The first one is a physical approach. Thereby, artifacts can be reproduced and corrected within the original reconstruction by using assumptions in a polychromatic forward projector. These assumptions could be the used spectrum, the detector response, the physical attenuation and scatter properties of the intersected materials. A second method is an empirical approach, which does not rely on much prior knowledge. This so‐called empirical beam hardening correction (EBHC) and the previously mentioned physical‐based technique are both relying on a segmentation of the present tissues inside the patient. The difficulty thereby is that beam hardening by itself, scatter, and other effects, which diminish the image quality also disturb the correct tissue classification and thereby reduce the accuracy of the two known classes of correction techniques. The herein proposed method works similar to the empirical beam hardening correction but does not require a tissue segmentation and therefore shows improvements on image data, which are highly degraded by noise and artifacts. Furthermore, the new algorithm is designed in a way that no additional calibration or parameter fitting is needed. Methods: To overcome the segmentation of tissues, the authors propose a histogram deformation of their primary reconstructed CT image. This step is essential for the proposed algorithm to be segmentation‐free (sf). This deformation leads to a nonlinear accentuation of higher CT‐values. The original volume and the gray value deformed volume are monochromatically forward projected. The two projection sets are then monomially combined and reconstructed to generate sets of basis volumes which are used for correction. This is done by maximization of the image flatness due to adding additionally a weighted sum of these basis images. sfEBHC is evaluated on polychromatic simulations, phantom measurements, and patient data. The raw data sets were acquired by a dual source spiral CT scanner, a digital volume tomograph, and a dual source micro CT. Different phantom and patient data were used to illustrate the performance and wide range of usability of sfEBHC across different scanning scenarios. The artifact correction capabilities are compared to EBHC. Results: All investigated cases show equal or improved image quality compared to the standard EBHC approach. The artifact correction is capable of correcting beam hardening artifacts for different scan parameters and scan scenarios. Conclusions: sfEBHC generates beam hardening‐reduced images and is furthermore capable of dealing with images which are affected by high noise and strong artifacts. The algorithm can be used to recover structures which are hardly visible inside the beam hardening‐affected regions.

Positron emission tomography (PET) of the thorax region is impaired by respiratory patient motion. To account for motion, the authors propose a new method for PET/magnetic resonance (MR) respiratory motion compensation (MoCo), which uses highly undersampled MR data with acquisition times as short as 1 min/bed.The proposed PET/MR MoCo method (4D jMoCo PET) uses radial MR data to estimate the respiratory patient motion employing MR joint motion estimation and image reconstruction with temporal median filtering. Resulting motion vector fields are incorporated into the system matrix of the PET reconstruction. The proposed approach is evaluated for the thorax region utilizing a PET/MR simulation with 1 min MR acquisition time and simultaneous PET/MR measurements of six patients with MR acquisition times of 1 and 5 min and radial undersampling factors of 11.2 and 2.2, respectively. Reconstruction results are compared to 3D PET, 4D gated PET and a standard MoCo method (4D sMoCo PET), which performs iterative image reconstruction and motion estimation sequentially. Quantitative analysis comprises the parameters SUVmean, SUVmax, full width at half-maximum/lesion volume, contrast and signal-to-noise ratio.For simulated PET data, our quantitative analysis shows that the proposed 4D jMoCo PET approach with temporal filtering achieves the best quantification accuracy of all tested reconstruction methods with a mean absolute deviation of 2.3% when compared to the ground truth. For measured PET patient data, the mean absolute deviation of 4D jMoCo PET using a 1 min MR acquisition for motion estimation is 2.1% relative to the 5 min MR acquisition. This demonstrates a robust behavior even in case of strong undersampling at MR acquisition times as short as 1 min. In contrast, 4D sMoCo PET shows considerable reduction of quantification accuracy for the 1 min MR acquisition time. Relative to 3D PET, the proposed 4D jMoCo PET approach with temporal filtering yields an average increase of SUVmean, SUVmax, and contrast of 29.9% and 13.8% for simulated and measured PET data, respectively.Employing artifact-robust motion estimation enables PET/MR respiratory MoCo with MR acquisition times as short as 1 min/bed improving PET image quality and quantification accuracy.

Purpose Dynamic CT perfusion (CTP) consists in repeated acquisitions of the same volume in different time steps, slightly before, during and slightly afterwards the injection of contrast media. Important functional information can be derived for each voxel, which reflect the local hemodynamic properties and hence the metabolism of the tissue. Different approaches are being investigated to exploit data redundancy and prior knowledge for noise reduction of such datasets, ranging from iterative reconstruction schemes to high dimensional filters. Methods We propose a new spatial bilateral filter which makes use of the k-means clustering algorithm and of an optimal calculated guiding image. We named the proposed filter as k-means clustering guided bilateral filter (KMGB). In this study, the KMGB filter is compared with the partial temporal non-local means filter (PATEN), with the time-intensity profile similarity (TIPS) filter, and with a new version derived from it, by introducing the guiding image (GB-TIPS). All the filters were tested on a digital in-house developed brain CTP phantom, were noise was added to simulate 80 kV and 200 mAs (default scanning parameters), 100 mAs and 30 mAs. Moreover, the filters performances were tested on 7 noisy clinical datasets with different pathologies in different body regions. The original contribution of our work is two-fold: first we propose an efficient algorithm to calculate a guiding image to improve the results of the TIPS filter, secondly we propose the introduction of the k-means clustering step and demonstrate how this can potentially replace the TIPS part of the filter obtaining better results at lower computational efforts. Results As expected, in the GB-TIPS, the introduction of the guiding image limits the over-smoothing of the TIPS filter, improving spatial resolution by more than 50%. Furthermore, replacing the time-intensity profile similarity calculation with a fuzzy k-means clustering strategy (KMGB) allows to control the edge preserving features of the filter, resulting in improved spatial resolution and CNR both for CT images and for functional maps. In the phantom study, the PATEN filter showed overall the poorest results, while the other filters showed comparable performances in terms of perfusion values preservation, with the KMGB filter having overall the best image quality. Conclusion In conclusion, the KMGB filter leads to superior results for CT images and functional maps quality improvement, in significantly shorter computational times compared to the other filters. Our results suggest that the KMGB filter might be a more robust solution for halved-dose CTP datasets. For all the filters investigated, some artifacts start to appear on the BF maps if one sixth of the dose is simulated, suggesting that no one of the filters investigated in this study might be optimal for such a drastic dose reduction scenario.

To explore the value and reproducibility of a novel magnetic resonance based attenuation correction (MRAC) using a CAIPIRINHA-accelerated T1-weighted Dixon 3D-VIBE sequence for whole-body PET/MRI compared to the clinical standard.The PET raw data of 19 patients from clinical routine were reconstructed with standard MRAC (MRACstd) and the novel MRAC (MRACcaipi), a prototype CAIPIRINHA accelerated Dixon 3D-VIBE sequence, both acquired in 19 s/bed position. Volume of interests (VOIs) for liver, lung and all voxels of the total image stack were created to calculate standardized uptake values (SUVmean) followed by inter-method agreement (Passing-Bablok regression, Bland-Altman analysis). A voxel-wise SUV comparison per patient was performed for intra-individual correlation between MRACstd and MRACcaipi. Difference images (MRACstd-MRACcaipi) of attenuation maps and SUV images were calculated. The image quality of in/opposed-phase water and fat images obtained from MRACcaipi was assessed by two readers on a 5-point Likert-scale including intra-class coefficients for inter-reader agreement.SUVmean correlations of VOIs demonstrated high linearity (0.95<Spearman's rho<1, p<0.0001, respectively), substantiated by voxel-wise SUV scatter-plots (1.79×108 pixels). Outliers could be explained by different physiological conditions between the scans such as different segmentation of air-containing tissue, lungs, kidneys, metal implants, diaphragm edge or small air bubbles in the gastrointestinal tracts that moved between MRAC acquisitions. Nasal sinuses and the trachea were better segmented in MRACcaipi. High-resolution T1w Dixon 3D VIBE images were acquired in all cases and could be used for PET/MRI fusion. MRACcaipi images were of high diagnostic quality (4.2±0.8) with 0.92-0.96 intra-class correlation.The novel prototype MRACcaipi extends the value for attenuation correction by providing a high spatial resolution DIXON-based dataset suited for diagnostic assessment towards time-efficient whole-body PET/MRI.

Abstract An evaluation of energy thresholding and acquisition mode for metal artifact reduction in Photon-counting detector CT (PCD-CT) compared to conventional energy-integrating detector CT (EID-CT) was performed. Images of a hip prosthesis phantom placed in a water bath were acquired on a scanner with PCD-CT and EID-CT (tube potentials: 100, 120 and 140 kV p ) and energy thresholds (above 55–75 keV) in Macro and Chess mode. Only high-energy threshold images (HTI) were used. Metal artifacts were quantified by a semi-automated segmentation algorithm, calculating artifact volumes, means and standard deviations of CT numbers. Images of a human cadaver with hip prosthesis were acquired on the PCD-CT in Macro mode as proof-of-concept. Images at 140 kV p showed less metal artifacts than 120 kV p or 100 kV p . HTI (70, 75 keV) had fewer artifacts than low energy thresholds (55, 60, 65 keV). Fewer artifacts were observed in the Macro-HTI (8.9–13.3%) for cortical bone compared to Chess-HTI (9.4–19.1%) and EID-CT (10.7–19.0%) whereas in bone marrow Chess-HTI (19.9–45.1%) showed less artifacts compared to Macro-HTI (21.9–38.3%) and EID-CT (36.4–54.9%). Noise for PCD-CT (56–81 HU) was higher than EID-CT (33–36 HU) irrespective of tube potential. High-energy thresholding could be used for metal artifact reduction in PCD-CT, but further investigation of acquisition modes depending on target structure is required.

Background and Purpose4D and midposition MRI could inform plan adaptation in lung and abdominal MR-guided radiotherapy. We present deep learning-based solutions to overcome long 4D-MRI reconstruction times while maintaining high image quality and short scan times.MethodsTwo 3D U-net deep convolutional neural networks were trained to accelerate the 4D joint MoCo-HDTV reconstruction. For the first network, gridded and joint MoCo-HDTV-reconstructed 4D-MRI were used as input and target data, respectively, whereas the second network was trained to directly calculate the midposition image. For both networks, input and target data had dimensions of 256 × 256 voxels (2D) and 16 respiratory phases. Deep learning-based MRI were verified against joint MoCo-HDTV-reconstructed MRI using the structural similarity index (SSIM) and the naturalness image quality evaluator (NIQE). Moreover, two experienced observers contoured the gross tumour volume and scored the images in a blinded study.ResultsFor 12 subjects, previously unseen by the networks, high-quality 4D and midposition MRI (1.25 × 1.25 × 3.3 mm3) were each reconstructed from gridded images in only 28 seconds per subject. Excellent agreement was found between deep-learning-based and joint MoCo-HDTV-reconstructed MRI (average SSIM ≥ 0.96, NIQE scores 7.94 and 5.66). Deep-learning-based 4D-MRI were clinically acceptable for target and organ-at-risk delineation. Tumour positions agreed within 0.7 mm on midposition images.ConclusionOur results suggest that the joint MoCo-HDTV and midposition algorithms can each be approximated by a deep convolutional neural network. This rapid reconstruction of 4D and midposition MRI facilitates online treatment adaptation in thoracic or abdominal MR-guided radiotherapy.

A collection of software applications dedicated to clinical x-ray computed tomography (CT) has been developed. These tools are designed for routine and scientific work and represent a complete platform, which is called syngo Explorer. The user can reconstruct, process, and view CT images in a personal computer environment independently of specialized hardware. To manage patient data and to allow full database access, the system uses the syngo platform (Siemens Medical Solutions, Erlangen, Germany), which serves as a framework for the management and visualization of Digital Imaging and Communications in Medicine (DICOM) images. Part of the philosophy behind syngo Explorer is to quickly provide specialized solutions and to be able to respond to user requests at once. Thus, syngo Explorer was built by choosing a modular setup that comprises standard and specific reconstruction algorithms as well as various artifact reduction techniques; it also helps one attain better insights into dose issues. Various raw data formats of commercial CT scanners are supported. Reconstruction algorithms equivalent to those on the manufacturer's scanner console are provided. A non-syngo Windows XP (Microsoft, Redmond, Wash) version of the application is available.

Purpose In order to mitigate motion‐induced artifacts, several motion compensation (MoCo) methods have been developed, which are either able to (a) compensate for severe artifacts, but utilize the data for the reconstruction of several cardiac phases, or (b) improve image quality of a single reconstruction with only moderate motion artifacts. We propose a method combining both benefits: dose efficiency by utilizing only the data needed for a single short‐scan reconstruction while still being able to compensate for severe artifacts. Methods We introduce a MoCo method, which we call PAM oCo, to improve the visualization of the coronary arteries of a standard coronary CT angiography exam by reducing motion artifacts. As a first step, we segment a region of interest covering a chosen coronary artery. We subdivide a volume covering the whole heart into several stacks, which are sub‐volumes, reconstructed from phase‐correlated short‐scan data acquired during different heart cycles. Motion‐compensated reconstruction is performed for each stack separately, based on partial angle reconstructions, which are derived by dividing the data corresponding to the segmented stack volume into several double‐overlapping sectors. We model motion along the coronary artery center line obtained from segmentation and the temporal dimension by a low‐degree polynomial and create a dense 3D motion vector field ( MVF ). The parameters defining the MVF are estimated by optimizing an image artifact measuring cost function and we employ a semi‐global optimization routine by re‐initializing the optimization multiple times. The algorithm was evaluated on the basis of a phantom measurement and clinical data. For the phantom measurement an artificial vessel equipped with calcified lesions mounted on a moving robot arm was measured, where typical coronary artery motion patterns for 70 bpm and 90 bpm have been applied. For analysis, we calculated the calcified volume V inside an ROI and measured the maximum vessel diameter d based on cross‐sectional views to compare the performances of standard reconstructions obtained via filtered backprojection ( FBP ) and PAM oCo reconstructions between 20% and 80% of the cardiac cycle. Further, the new algorithm was applied to six clinical cases of patients with heart rates between 50 bpm and 74 bpm. Standard FBP , PAM oCo reconstructions were performed and compared to best phase FBP reconstructions and another MoCo algorithm, which is based on motion artifact metrics ( MAM ), via visual inspection. Results In case of the phantom measurement we found a strong dependence of V and d on the cardiac phase in case of the FBP reconstructions. When applying PAM oCo, V and d became almost constant due to a better discrimination from calcium to vessel and water background and values close to the ground truth have been derived. In the clinical study we chose reconstructions showing strong motion artifacts and observed a substantially improved delineation of the coronary arteries in PAM oCo reconstructions compared to the standard FBP reconstructions and also the MAM images, confirming the findings of the phantom measurement. Conclusions Due to the fast reconstruction of PAM oCo images and the introduction of a new motion model, we were able to re‐initialize the optimization routine at pre‐selected parameter sets and thereby increase the potential of the MAM algorithm. From the phantom measurement we conclude that PAM oCo performed almost equally well in all cardiac phases and suggest applying the PAM oCo algorithm for single source systems in case of patients with high or irregular heart rates.

Accurate PET quantification demands attenuation correction (AC) for both patient and hardware attenuation of the 511 keV annihilation photons. In hybrid PET/MR imaging, AC for stationary hardware components such as patient table and MR head coil is straightforward, employing CT-derived attenuation templates. AC for flexible hardware components such as MR-safe headphones and MR radiofrequency (RF) surface coils is more challenging. Registration-based approaches, aligning CT-based attenuation templates with the current patient position, have been proposed but are not used in clinical routine. Ignoring headphone or RF coil attenuation has been shown to result in regional activity underestimation values of up to 18%. We propose to employ the maximum-likelihood reconstruction of attenuation and activity (MLAA) algorithm to estimate the attenuation of flexible hardware components. Starting with an initial attenuation map not including flexible hardware components, the attenuation update of MLAA is applied outside the body outline only, allowing to estimate hardware attenuation without modifying the patient attenuation map. Appropriate prior expectations on the attenuation coefficients are incorporated into MLAA. The proposed method is investigated for non-TOF PET phantom and 18F-FDG patient data acquired with a clinical PET/MR device, using headphones or RF surface coils as flexible hardware components. Although MLAA cannot recover the exact physical shape of the hardware attenuation maps, the overall attenuation of the hardware components is accurately estimated. Therefore, the proposed algorithm significantly improves PET quantification. Using the phantom data, local activity underestimation when neglecting hardware attenuation was reduced from up to 25% to less than 3% under- or overestimation as compared to reference scans without hardware present or to CT-derived AC. For the patient data, we found an average activity underestimation of 7.9% evaluated in the full brain and of 6.1% for the abdominal region comparing the uncorrected case with MLAA. MLAA is able to provide accurate estimations of the attenuation of flexible hardware components and can therefore be used to significantly improve PET quantification. The proposed approach can be readily incorporated into clinical workflow.

Purpose: To introduce and evaluate an increment matrix approach (IMA) describing the signal statistics of energy‐selective photon counting detectors including spatial–spectral correlations between energy bins of neighboring detector pixels. The importance of the occurring correlations for image‐based material decomposition is studied. Methods: An IMA describing the counter increase patterns in a photon counting detector is proposed. This IMA has the potential to decrease the number of required random numbers compared to Monte Carlo simulations by pursuing an approach based on convolutions. To validate and demonstrate the IMA, an approximate semirealistic detector model is provided, simulating a photon counting detector in a simplified manner, e.g., by neglecting count rate‐dependent effects. In this way, the spatial–spectral correlations on the detector level are obtained and fed into the IMA. The importance of these correlations in reconstructed energy bin images and the corresponding detector performance in image‐based material decomposition is evaluated using a statistically optimal decomposition algorithm. Results: The results of IMA together with the semirealistic detector model were compared to other models and measurements using the spectral response and the energy bin sensitivity, finding a good agreement. Correlations between the different reconstructed energy bin images could be observed, and turned out to be of weak nature. These correlations were found to be not relevant in image‐based material decomposition. An even simpler simulation procedure based on the energy bin sensitivity was tested instead and yielded similar results for the image‐based material decomposition task, as long as the fact that one incident photon can increase multiple counters across neighboring detector pixels is taken into account. Conclusions: The IMA is computationally efficient as it required about 10 2 random numbers per ray incident on a detector pixel instead of an estimated 10 8 random numbers per ray as Monte Carlo approaches would need. The spatial–spectral correlations as described by IMA are not important for the studied image‐based material decomposition task. Respecting the absolute photon counts and thus the multiple counter increases by a single x‐ray photon, the same material decomposition performance could be obtained with a simpler detector description using the energy bin sensitivity.

Time-resolved volumetric magnetic resonance imaging (4DMRI) offers the potential to analyze 3D motion with high soft-tissue contrast without additional imaging dose. We use 4DMRI to investigate the interplay effect for pencil beam scanning (PBS) proton therapy of pancreatic cancer and to quantify the dependency of residual interplay effects on the number of treatment fractions. Based on repeated 4DMRI datasets for nine pancreatic cancer patients, synthetic 4DCTs were generated by warping static 3DCTs with 4DMRI deformation vector fields. 4D dose calculations for scanned proton therapy were performed to quantify the interplay effect by CTV coverage (v95) and dose homogeneity (d5/d95) for incrementally up to 28 fractions. The interplay effect was further correlated to CTV motion characteristics. For quality assurance, volume and mass conservation were evaluated by Jacobian determinants and volume-density comparisons. For the underlying patient cohort with CTV motion amplitudes < 15 mm, we observed significant correlations between CTV motion amplitudes and both the length of breathing cycles and the interplay effect. For individual fractions, tumor underdosage down to v95 = 70% was observed with pronounced dose heterogeneity (d5/d95 = 1.3). For full × 28 fractionated treatments, we observed a mitigation of the interplay effect with increasing fraction numbers. On average, after seven fractions, a CTV coverage with 95–107% of the prescribed dose was reached with sufficient dose homogeneity. For organs at risk, no significant differences were found between the static and accumulated dose plans for 28 fractions. Intrafractional organ motion exhibits a large interplay effect for PBS proton therapy of pancreatic cancer. The interplay effect correlates with CTV motion, but can be mitigated efficiently by fractionation, mainly due to different breathing starting phases in fractionated treatments. For hypofractionated treatments, a further restriction of motion may be required. Repeated 4DMRI measurements are a viable tool for pre- and post-treatment evaluations of the interplay effect.

Purpose Four‐dimensional cone‐beam computed tomography (4D CBCT) has been developed to provide a sequence of phase‐resolved reconstructions in image‐guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts because the 4D CBCT reconstruction process is an extreme sparse‐view CT procedure wherein only under‐sampled projections are used for the reconstruction of each phase. To obtain a set of 4D CBCT images achieving both high spatial and temporal resolution, we propose an algorithm by providing a high‐quality initial image at the beginning of the iterative reconstruction process for each phase to guide the final reconstructed result toward its optimal solution. Methods The proposed method consists of three steps to generate the initial image. First, a prior image is obtained by an iterative reconstruction method using the measured projections of the entire set of 4D CBCT images. The prior image clearly shows the appearance of structures in static regions, although it contains blurring artifacts in motion regions. Second, the robust principal component analysis (RPCA) model is adopted to extract the motion components corresponding to each phase‐resolved image. Third, a set of initial images are produced by the proposed linear estimation model that combines the prior image and the RPCA‐decomposed motion components. The final 4D CBCT images are derived from the simultaneous algebraic reconstruction technique (SART) equipped with the initial images. Qualitative and quantitative evaluations were performed by using two extended cardiac‐torso (XCAT) phantoms and two sets of patient data. Several state‐of‐the‐art 4D CBCT algorithms were performed for comparison to validate the performance of the proposed method. Results The image quality of phase‐resolved images is greatly improved by the proposed method in both phantom and patient studies. The results show an outstanding spatial resolution, in which streaking artifacts are suppressed to a large extent, while detailed structures such as tumors and blood vessels are well restored. Meanwhile, the proposed method depicts a high temporal resolution with a distinct respiratory motion change at different phases. For simulation phantom, quantitative evaluations of the simulation data indicate that an average of 36.72% decrease at EI phase and 42% decrease at EE phase in terms of root‐mean‐square error (RMSE) are achieved by our method when comparing with PICCS algorithm in Phantom 1 and Phantom 2. In addition, the proposed method has the lowest entropy and the highest normalized mutual information compared with the existing methods in simulation experiments, such as PRI, RPCA‐4DCT, SMART, and PICCS. And for real patient cases, the proposed method also achieves the lowest entropy value compared with the competitive method. Conclusions The proposed algorithm can generate an optimal initial image to improve iterative reconstruction performance. The final sequence of phase‐resolved volumes guided by the initial image achieves high spatiotemporal resolution by eliminating motion‐induced artifacts. This study presents a practical 4D CBCT reconstruction method with leading image quality.

Purpose In clinics, only iodine‐ and barium‐based contrast agents are currently used for contrast‐enhanced x‐ray computed tomography (CT). Recently, the introduction of new photon‐counting (PC) detectors increased the interest in developing new contrast agents based on heavier elements. These elements may provide more contrast and spectral information compared to iodine and barium thanks to their k‐edges at higher energies. In this paper, the potential of high‐Z elements in contrast‐enhanced CT was evaluated for different patient sizes and x‐ray spectra using a PC detector. Methods An adult liver phantom with five high‐Z element solutions (iodine, gadolinium, ytterbium, tungsten, and bismuth) was scanned with a whole‐body photon‐counting computed tomography (PCCT) prototype. For each element, the contrast‐to‐noise ratio at unit concentration and at unit dose (CNRCD) was evaluated in low threshold images ( ) as function of the tube voltage (80, 100, 120, and 140 kV) and in bin images (tube voltage = 120 kV) as function of the higher threshold ( and ). Simulations were performed for validation with measurements and to investigate more elements (cerium and gold), different patient sizes (infant, adult, and obese), and spectrum filtration (with and without 0.4‐mm tin filter). The dose reductions associated with the CNRCD improvements over iodine were quantified as well. Results CNRCD improvements and dose reductions depend on the investigated scenario. For the infant phantom, dose reductions around 30% were reached using cerium or gadolinium in combination with the tin filter. For the adult and obese phantom, reductions around 50% were provided by gadolinium or ytterbium in combination with the tin filter. Independently of the high‐Z element, the CNRCD of two optimally combined bin images was higher than the CNRCD of the low threshold image. Good agreement was found between measurements and simulations. Conclusions Between the investigated elements, gadolinium resulted to have the highest potential as novel contrast agent in PCCT, providing significant dose reductions for all patient sizes. Compared to the other elements, the implementation of gadolinium as CT contrast agent may be facilitated since it is already deployed as contrast agents for magnetic resonance imaging.

Over the last decade, a fundamentally new type of computed tomography (CT) detectors has proved its superior capabilities in both physical and preclinical evaluations and is now approaching the stage of clinical practice. These detectors are able to discriminate single photons and quantify their energy and are hence called photon-counting detectors. Among the promising benefits of this technology are improved radiation dose efficiency, increased contrast-to-noise ratio, reduced metal artifacts, improved spatial resolution, simultaneous multi-energy acquisitions, and the prospect of multi-phase imaging within a single acquisition using multiple contrast agents. Taking the conventional energy-integrating detectors as a reference, the authors demonstrate the technical principles of this new technology and provide phantom and patient images acquired by a whole-body photon-counting CT. These images serve as a basis for discussing the potential future of clinical CT.Im vergangenen Jahrzehnt hat ein grundlegend neuer Detektortyp in der Computertomographie (CT) seine überlegene Leistungsfähigkeit gezeigt, sowohl in physikalischen als auch in präklinischen Prüfungen. Nun nähert er sich der klinischen Anwendung. Diese Detektoren können einzelne Photonen voneinander unterscheiden und deren Energie messen. Entsprechend werden sie als Photonenzählende Detektoren bezeichnet. Zu den vielversprechenden Vorzügen der neuen Technik gehören die verbesserte Strahlendosiseffizienz, ein erhöhtes Kontrast-zu-Rausch-Verhältnis, reduzierte Metallartefakte, eine verbesserte räumliche Auflösung, simultane Multi-Energie-Aufnahmen und die Aussicht auf eine Multiphasenbildgebung mit einer einzigen Aufnahme unter Verwendung mehrerer Kontrastmittel. Mit den konventionellen energieintegrierenden Detektoren als Referenz werden im vorliegenden Beitrag die technischen Grundprinzipien der neuen Technik erläutert. Es werden Phantom- und Patientenbilder präsentiert, die mit photonenzählender Ganzkörper-CT angefertigt wurden. Ausgehend von diesen Bildern werden mögliche Zukunftsaussichten der klinischen CT diskutiert.

Abstract Clinical photon-counting CT (PCCT) offers a spatial resolution of about 200 µm and might allow for acquisitions close to conventional dental CBCTs. In this study, the capabilities of this new system in comparison to dental CBCTs shall be evaluated. All 8 apical osteolysis identified in CBCT were identified by both readers in all three PCCT scan protocols. Mean visibility scores showed statistical significant differences for root canals(p = 0.0001), periodontal space(p = 0.0090), cortical(p = 0.0003) and spongious bone(p = 0.0293) in favor of high and medium dose PCCT acquisitions. Overall, both devices showed excellent image quality of all structures assessed. Interrater-agreement showed high values for all protocols in all structures. Bland–Altman plots revealed a high concordance of both modalities with the reference measurements. In vitro, ultra-high resolution PCCT can reliably identify different diagnostic entities and structures relevant for dental diagnostics similar to conventional dental CBCT with similar radiation dose. Acquisitions of five cadaveric heads were performed in an experimental CT-system containing an ultra-high resolution PC detector (0.25 mm pixel size in isocenter) as well as in a dental CBCT scanner. Acquisitions were performed using dose levels of 8.5 mGy, 38.0 mGy and 66.5 mGy (CTDI16cm) in case of PCCT and of 8.94 mGy (CTDI16cm) in case of CBCT. The quality of delineation of hard tissues, root-canals, periodontal-space as well as apical osteolysis was assessed by two readers. Mean visibility scores and interrater-agreement (overall agreement (%)) were calculated. Vertical bone loss (bl) and thickness (bt) of the buccal bone lamina of 15 lower incisors were measured and compared to reference measurements by ore microscopy and clinical probing.

X-ray computed tomography (CT) is a cardinal tool in clinical practice. It provides cross-sectional images within seconds. The recent introduction of clinical photon-counting CT allowed for an increase in spatial resolution by more than a factor of two resulting in a pixel size in the center of rotation of about 150 µm. This level of spatial resolution is in the order of dedicated preclinical micro-CT systems. However so far, the need for different dedicated clinical and preclinical systems often hinders the rapid translation of early research results to applications in men. This drawback might be overcome by ultra-high resolution (UHR) clinical photon-counting CT unifying preclinical and clinical research capabilities in a single machine. Herein, the prototype of a clinical UHR PCD CT (SOMATOM CounT, Siemens Healthineers, Forchheim, Germany) was used. The system comprises a conventional energy-integrating detector (EID) and a novel photon-counting detector (PCD). While the EID provides a pixel size of 0.6 mm in the centre of rotation, the PCD provides a pixel size of 0.25 mm. Additionally, it provides a quantification of photon energies by sorting them into up to four distinct energy bins. This acquisition of multi-energy data allows for a multitude of applications, e.g. pseudo-monochromatic imaging. In particular, we examine the relation between spatial resolution, image noise and administered radiation dose for a multitude of use-cases. These cases include ultra-high resolution and multi-energy acquisitions of mice administered with a prototype bismuth-based contrast agent (nanoPET Pharma, Berlin, Germany) as well as larger animals and actual patients. The clinical EID provides a spatial resolution of about 9 lp/cm (modulation transfer function at 10%, MTF10%) while UHR allows for the acquisition of images with up to 16 lp/cm allowing for the visualization of all relevant anatomical structures in preclinical and clinical specimen. The spectral capabilities of the system enable a variety of applications previously not available in preclinical research such as pseudo-monochromatic images. Clinical ultra-high resolution photon-counting CT has the potential to unify preclinical and clinical research on a single system enabling versatile imaging of specimens and individuals ranging from mice to man.

At the advent of multislice computed tomography (CT) a variety of approximate cone-beam algorithms have been proposed suited for reconstruction of small cone-angle CT data in a spiral mode of operation. The goal of this study is to identify a practical and efficient approximate cone-beam method, extend its potential for medical use, and demonstrate its performance at medium cone-angles required for area detector CT. The authors investigate two different approximate single-slice rebinning algorithms for cone-beam CT: the multirow Fourier reconstruction (MFR) and an extension of the advanced single-slice rebinning method (ASSR), which combines the idea of ASSR with a z-filtering approach. Thus, both algorithms, MFR and ASSR, are formulated in the framework of z-filtering using optimized spiral interpolation algorithms. In each view, X-ray samples to be used for reconstruction are identified, which describe an approximation to a virtual reconstruction plane. The performance of approximate reconstruction should improve as the virtual reconstruction plane better fits the spiral focus path. The image quality of the respective reconstruction is assessed with respect to image artifacts, spatial resolution, contrast resolution, and image noise. It turns out that the ASSR method using tilted reconstruction planes is a practical and efficient algorithm, providing image quality comparable to that of a single-row scanning system even with a 46-row detector at a table feed of 64 mm. Both algorithms tolerate any table feed below the maximum value associated to the detector height. Due to the z-filter approach, all detector data sampled can be used for image reconstruction.

We present a novel approximate image reconstruction technique for helical cone-beam CT, called the Advanced Multiple Plane Reconstruction (AMPR). The method is an extension of the ASSR algorithm presented in Medical Physics vol. 27, no. 4, 2000 by Kachelriess et al. In the ASSR, the pitch is fixed to a certain value and dose usage is not optimum. These limitations have been overcome in the AMPR algorithm by reconstructing several image planes from any given half scan range of projection angles. The image planes are tilted in two orientations so as to optimally use the data available on the detector. After reconstruction of several sets of tilted images, a subsequent interpolation step reformats the oblique image planes to a set of voxels sampled on a cartesian grid. Using our novel approach on a scanner with 16 slices, we can achieve image quality superior to what is currently a standard for four-slice scanners. Dose usage in the order of 95% for all pitch values can be achieved. We present simulations of semi-antropomorphic phantoms using a standard CT scanner geometry and a 16 slice design.

CT scanners acquire noisy data at discrete sample positions. Typically, a convention of how to continue these data from discrete integer positions to the continuous domain must be applied during processing. We study the properties of three typical one‐dimensional spatial domain interpolation algorithms in terms of a cost or quality factor . This figure of merit is a function of spatial resolution, data noise, and dose and is used to optimize detector design. Spatial resolution is defined as either mean square width or as the full width at half maximum of the point spread function (PSF). Our results show that a trapezoidal interpolation algorithm is optimal for the high resolution domain (relative to the detector aperture size ) and should be replaced by a triangular or Gaussian interpolation function for spatial resolutions of about or larger; these result in bell‐shaped PSFs. Assuming such a hybrid algorithm we find a 1.5‐fold increase of —this is equivalent to 50% improved dose usage—when smoothing the data to a spatial resolution of or more compared to a highest resolution reconstruction. Therefore it is advisable to use detectors of one‐third of the size of the desired spatial resolution and to compensate for the 1.5‐fold increase in by reducing dose by 33%. Under the presence of moderately sized septa (e.g., 10% of the spatial resolution element size) the benefit of optimizing still lies in the order of 30% improved dose usage; in that case the detector size should be on the order of and a dose reduction of 23% can be achieved. Again, bell‐shaped PSFs show a better tradeoff between noise and resolution for a given dose than rectangular‐shaped PSFs. The general interpretation of our results is that the degree of freedom of choosing the weighting or interpolation function for a given resolution is large for small detectors and small for large detectors. Thus systems with small have a higher potential of optimization compared to systems with large . Similarly, detector binning, which corresponds to replacing by , should be avoided. Note that the figures reported correspond to a one‐dimensional interpolation. Two‐dimensional detectors typically separate and resulting quality factors can be easily obtained by multiplication. Then, is expected to improve by a factor of without septa and by a factor of with septa. This indicates that dose can be reduced by about 56% and about 41%, respectively. Our findings are general and not restricted to CT. They can be readily applied to medical or nonmedical imaging devices and digital detectors and they may also turn out to be useful in other fields.

In classical x-ray CT, the diameter of the field of measurement (FOM) must not fall below the transversal diameter of the patient or specimen. Thereby, the ratio of the diameter of FOM and the number of transversal detector elements typically defines the spatial resolution. The authors aim at improving the spatial resolution within a region of interest (ROI) by a factor of 10-100 while maintaining artifact-free CT image reconstruction inside and outside the ROI. Two novel methods are proposed for artifact-free reconstruction of the truncated ROI scan (data weighting method and data filtering method) and compared with the gold standard (data completion method) for this problem.First, an overview scan with low spatial resolution and a large FOM that exceeds the object transversally is performed. Second, a high-resolution scan is performed, where the scanner's magnification is changed such that the FOM matches the ROI at the cost of laterally truncated projection data. The gold standard is forward projecting the low-resolution scan on the rays missing in the high-resolution scan. The authors propose the data filtering method, which uses the low-resolution reconstruction and calculates a high frequency correction term from the high-resolution scan, and the data weighting method, which reconstructs the truncated high-resolution data and calculates a detruncation image from the low-resolution data.The methods are compared using a simulation of the Forbild head phantom and a measurement of a spinal disk implant. The results of the data weighting method and the data completion method show the same image quality. The data filtering method yields slightly inferior image quality that may still be sufficient for many applications. Both new methods considerably outperform the data completion method regarding the computational load.The new ROI reconstruction methods are superior to the gold standard regarding the computational load. Comparing the image quality with the gold standard, the data filtering method is slightly inferior and the data weighting method yields equal quality.

Purpose: The authors propose a novel method for misalignment estimation of micro‐CT scanners using an adaptive genetic algorithm. Methods: The proposed algorithm is able to estimate the rotational geometry, the direction vector of table movement and the displacement between different imaging threads of a dual source or even multisource scanner. The calibration procedure does not rely on dedicated calibration phantoms and a sequence scan of a single metal bead is sufficient to geometrically calibrate the whole imaging system for spiral, sequential, and circular scan protocols. Dual source spiral and sequential scan protocols in micro‐computed tomography result in projection data that—besides the source and detector positions and orientations—also require a precise knowledge of the table direction vector to be reconstructed properly. If those geometric parameters are not known accurately severe artifacts and a loss in spatial resolution appear in the reconstructed images as long as no geometry calibration is performed. The table direction vector is further required to ensure that consecutive volumes of a sequence scan can be stitched together and to allow the reconstruction of spiral data at all. Results: The algorithm's performance is evaluated using simulations of a micro‐CT system with known geometry and misalignment. To assess the quality of the algorithm in a real world scenario the calibration of a micro‐CT scanner is performed and several reconstructions with and without geometry estimation are presented. Conclusions: The results indicate that the algorithm successfully estimates all geometry parameters, misalignment artifacts in the reconstructed volumes vanish, and the spatial resolution is increased as can be shown by the evaluation of modulation transfer function measurements.

Image-guided interventions are an increasingly important part of clinical minimally invasive procedures. However, up to now they cannot be performed under 4D (3D + time) guidance due to the exceedingly high x-ray dose. In this work we investigate the applicability of compressed sensing reconstructions for highly undersampled CT datasets combined with the incorporation of prior images in order to yield low dose 4D intervention guidance. We present a new reconstruction scheme prior image dynamic interventional CT (PrIDICT) that accounts for specific image features in intervention guidance and compare it to PICCS and ASD-POCS. The optimal parameters for the dose per projection and the numbers of projections per reconstruction are determined in phantom simulations and measurements. In vivo experiments in six pigs are performed in a cone-beam CT; measured doses are compared to current gold-standard intervention guidance represented by a clinical fluoroscopy system. Phantom studies show maximum image quality for identical overall doses in the range of 14 to 21 projections per reconstruction. In vivo studies reveal that interventional materials can be followed in 4D visualization and that PrIDICT, compared to PICCS and ASD-POCS, shows superior reconstruction results and fewer artifacts in the periphery with dose in the order of biplane fluoroscopy. These results suggest that 4D intervention guidance can be realized with today's flat detector and gantry systems using the herein presented reconstruction scheme.

Metal artifacts in computed tomography CT images are one of the main problems in radiation oncology as they introduce uncertainties to target and organ at risk delineation as well as dose calculation. This study is devoted to metal artifact reduction (MAR) based on the monoenergetic extrapolation of a dual energy CT (DECT) dataset. In a phantom study the CT artifacts caused by metals with different densities: aluminum (ρ Al=2.7 g/cm(3)), titanium (ρ Ti=4.5 g/cm(3)), steel (ρ steel=7.9 g/cm(3)) and tungsten (ρ W=19.3g/cm(3)) have been investigated. Data were collected using a clinical dual source dual energy CT (DECT) scanner (Siemens Sector Healthcare, Forchheim, Germany) with tube voltages of 100 kV and 140 kV(Sn). For each tube voltage the data set in a given volume was reconstructed. Based on these two data sets a voxel by voxel linear combination was performed to obtain the monoenergetic data sets. The results were evaluated regarding the optical properties of the images as well as the CT values (HU) and the dosimetric consequences in computed treatment plans. A data set without metal substitute served as the reference. Also, a head and neck patient with dental fillings (amalgam ρ=10 g/cm(3)) was scanned with a single energy CT (SECT) protocol and a DECT protocol. The monoenergetic extrapolation was performed as described above and evaluated in the same way. Visual assessment of all data shows minor reductions of artifacts in the images with aluminum and titanium at a monoenergy of 105 keV. As expected, the higher the densities the more distinctive are the artifacts. For metals with higher densities such as steel or tungsten, no artifact reduction has been achieved. Likewise in the CT values, no improvement by use of the monoenergetic extrapolation can be detected. The dose was evaluated at a point 7 cm behind the isocenter of a static field. Small improvements (around 1%) can be seen with 105 keV. However, the dose uncertainty remains of the order of 10% to 20%. Thus, the improvement is not significant for radiotherapy planning. For amalgam with a density between steel and tungsten, monoenergetic data sets of a patient do not show substantial artifact reduction. The local dose uncertainties around the metal artifact determined for a static field are of the order of 5%. Although dental fillings are smaller than the phantom inserts, metal artifacts could not be reduced effectively. In conclusion, the image based monoenergetic extrapolation method does not provide efficient reduction of the consequences of CT-generated metal artifacts for radiation therapy planning, but the suitability of other MAR methods will be subsequently studied.

Multislice spiral computed tomography (MSCT) requires an interpolation between adjacent detector rows during backprojection. Not satisfying the Nyquist sampling condition along the z-axis results in aliasing effects, also known as windmill artifacts. These image distortions are characterized by bright streaks diverging from high contrast structures.The z-flying focal spot (zFFS) is a well-established hardware-based solution that aims to double the sampling rate in longitudinal direction and therefore reduce aliasing artifacts. However, given the technical complexity of the zFFS, this work proposes a deep learning-based approach as an alternative solution.We propose a supervised learning approach to perform a mapping between input projections and the corresponding rows required for double sampling in the z-direction. We present a comprehensive evaluation using both a clinical dataset obtained using raw data from 40 real patient scans acquired with zFFS and a synthetic dataset consisting of 100 simulated spiral scans using a phantom specifically designed for our problem. For the clinical dataset, we utilized 32 scans as training set and 8 scans as validation set, whereas for the synthetic dataset, we used 80 scans for training and 20 scans for validation purposes. Both qualitative and quantitative assessments are conducted on a test set consisting of nine real patient scans and six phantom measurements to validate the performance of our approach. A simulation study was performed to investigate the robustness against different scan configurations in terms of detector collimation and pitch value.In the quantitative comparison based on clinical patient scans from the test set, all network configurations show an improvement in the root mean square error (RMSE) of approximately 20% compared to neglecting the doubled longitudinal sampling by the zFFS. The results of the qualitative analysis indicate that both clinical and synthetic training data can reduce windmill artifacts through the application of a correspondingly trained network. Together with the qualitative results from the test set phantom measurements it is emphasized that a training of our method with synthetic data resulted in superior performance in windmill artifact reduction.Deep learning-based raw data interpolation has the potential to enhance the sampling in z-direction and thus minimize aliasing effects, as it is the case with the zFFS. Especially a training with synthetic data showed promising results. While it may not outperform zFFS, our method represents a beneficial solution for CT scanners lacking the necessary hardware components for zFFS.

Recent advances in minimally invasive vascular disease treatments have led to the use of interventional tools like guidewires and stents,¹ guided by fluoroscopy with high temporal resolution but limited depth information. To address this limitation, there is a growing interest in 3D image guidance, or 4D interventional guidance, which involves displaying a series of 3D images during procedures. However, implementing X-ray-based 4D interventional guidance requires a high-temporal-resolution reconstruction algorithm with minimal dose per 3D reconstruction. V¨oth et al.² proposed, based on prior work of Eulig et al.,^{3, 4} an algorithm for the 3D reconstruction of interventional material from only two newly acquired X-ray images. Their pipeline utilizes the deep tool extraction (DTE) algorithm to compute interventional material images, which are then back-projected into a volume. A 3D U-Net⁵ called the deep tool reconstruction (DTR) transforms these backprojections into 3D reconstructions of the interventional material. While the pipeline shows impressive 3D reconstruction quality, it occasionally outputs false positives or negatives. In this work, we enhance the temporal information utilization by feeding the reconstructions of previous time steps as additional inputs to the DTR, improving the Dice coefficient from 71.21% to 76.84% on a simulated guidewire dataset.

Multislice spiral CT offers many new possibilities for clinical CT imaging. Drastically increased scan speeds and z-resolution, respectively, as well as applications such as cardiac CT that have become feasible for the first time in routine clinical use. The concept of multiple simultaneously acquired slices yields image quality equivalent or better than single-slice spiral CT. Especially, there is no dependence on spiral pitch, neither with regard to noise nor to slice sensitivity. The reconstructed slice width can be chosen freely and retrospectively, which offers additional flexibility when evaluating optimal protocols for various kinds of examinations. Three-dimensional isotropic resolution can be achieved routinely with examinations fast enough to scan in a single breath hold (Fig. 9). Without any drawbacks in image quality, MSCT in combination with online tube current modulation can reduce patient dose. In some body regions, dose is decreased to 50% compared to a scan with constant tube current. One of the most promising new applications is the dedicated ECG-gated cardiac interpolation 180 degrees MCI, which allows four-dimensional (4-D) imaging of the heart. The complete beating heart can be reconstructed in well-defined phases of the heart cycle, thereby adding high temporal resolution to isotropic 3-D spatial resolution (for more examples refer to http://www.imp.uni-erlangen.de/e/research/cardio/).

Phase-correlated CT, as it is used for cardiac imaging, is the most popular and the most important but also the most demanding special CT application in the clinical routine, today. Basically, it fulfills the four-dimensional imaging task of depicting a quasiperiodically moving object at any desired motion phase with significantly reduced motion artifacts. Although image quality with phase-correlated reconstruction is far better than with standard reconstruction, there are motion artifacts remaining and improvements of temporal resolution are required. As a well-known alternative to simply decreasing rotation time, we consider a spiral cone-beam CT scanner that has G x-ray guns and detectors mounted. We call this a multisource or a multithreaded CT scanner. Aiming for improved temporal resolution the relative temporal resolution tau, which measures the fraction of a motion period that enters the image, is studied as a function of the motion rate (heart rate) and the degree of scan overlap (pitch value) for various configurations. The parameters to optimize are the number of threads G and the interthread parameters delta alpha and delta z, which are the angular and the longitudinal separation between adjacent threads, respectively. To demonstrate the improvements approximate image reconstruction of multithreaded raw data is performed by using a generalization of the extended parallel back projection cone-beam reconstruction algorithm [Med. Phys. 31(6), 1623-1641 (2004)] to the case of multithreaded CT. Reconstructions of a simulated cardiac motion phantom and of simulated semi-antropomorphic phantoms are presented for two and three threads and compared to the single-threaded case to demonstrate the potential of multithreaded cardiac CT. Patient data were acquired using a clinical double-threaded CT scanner to validate the theoretical results. The optimum angle delta alpha between the tubes is 90 degrees for a double-threaded system, and for triple-threaded scanners it is 60 degrees or 120 degrees. In all cases, delta z = 0 results as an optimum, which means that the threads should be mounted in the same transversal plane. However, the dependency of the temporal resolution on delta z is very weak and a longitudinal separation delta z not = 0 would not deteriorate image quality. The mean temporal resolution achievable with an optimized multithreaded CT scanner is a factor of G better than the mean temporal resolution obtained with a single-threaded scanner. The standard reconstructions showed decreased cone-beam artifacts with multithreaded CT compared to the single-threaded case. Our phase-correlated reconstructions demonstrate that temporal resolution is significantly improved with multithreaded CT. The clinical patient data confirm our results.

Purpose: Cardiac CT achieves its high temporal resolution by lowering the scan range from to plus fan angle (partial scan). This, however, introduces CT‐value variations, depending on the angular position of the range. These partial scan artifacts are of the order of a few HU and prevent the quantitative evaluation of perfusion measurements. The authors present the new algorithm partial scan artifact reduction (PSAR) that corrects a dynamic phase‐correlated scan without a priori information. Methods: In general, a full scan does not suffer from partial scan artifacts since all projections in [0, ] contribute to the data. To maintain the optimum temporal resolution and the phase correlation, PSAR creates an artificial full scan by projectionwise averaging a set of neighboring partial scans from the same perfusion examination (typically phase‐correlated partial scans distributed over and ). Corresponding to the angular range of each partial scan, the authors extract virtual partial scans from the artificial full scan . A standard reconstruction yields the corresponding images , , and . Subtracting the virtual partial scan image from the artificial full scan image yields an artifact image that can be used to correct the original partial scan image: , where is the corrected image. Results: The authors evaluated the effects of scattered radiation on the partial scan artifacts using simulated and measured water phantoms and found a strong correlation. The PSAR algorithm has been validated with a simulated semianthropomorphic heart phantom and with measurements of a dynamic biological perfusion phantom. For the stationary phantoms, real full scans have been performed to provide theoretical reference values. The improvement in the root mean square errors between the full and the partial scans with respect to the errors between the full and the corrected scans is up to 54% for the simulations and 90% for the measurements. Conclusions: The phase‐correlated data now appear accurate enough for a quantitative analysis of cardiac perfusion.

In circular cone-beam CT the Feldkamp [Feldkamp-Davis-Kress (FDK)] algorithm is the most prominent image reconstruction algorithm. For example, in radiation oncology images reconstructed with the Feldkamp algorithm are used for accurate patient positioning. The scan and reconstruction volumes are limited by the size of the flat panel detector. Flat panel detectors, however, are expensive and difficult to manufacture in large size. For numerous treatment techniques, extending this scan volume would be very beneficial. In most applications, data from 360 degrees or more are available. However, usually only those slices are reconstructed where each pixel is seen under the full 360 degree range. Yet for a 360 degree scan there are regions that are seen by less than 360 degrees, namely, those that lie further off the plane of the circular source trajectory. Performing a reconstruction also for those slices where all voxels are seen at least by 180 degrees will extend the z range and therefore increase the dose usage. In this work a new method is presented that reconstructs also those slices where some or all pixels receive less than 360 degrees but at least 180 degrees of the data. The procedure significantly increases the longitudinal range of the reconstructed volume. As opposed to the existing techniques, the proposed method does not necessitate any multiple convolutions or multiple backprojections, lending itself therefore for a very efficient implementation. To validate the abilities of the extended reconstruction, the authors performed an evaluation of the image quality by using simulated and measured CT data. The method shows good image quality on simulated phantom data as well as on clinical patient scans. Image noise and spatial resolution behave as expected. This means that the noise equals FDK values in the normal region and increases in the extended region due to reduced data redundancies. The extended Feldkamp demonstrates its ability to extend the reconstructable z range and appears to be useful in clinical practice.

Purpose: X-ray CT measures the attenuation of polychromatic x-rays through an object. The rawdata acquired, which are the negative logarithm of the relative x-ray intensity behind the patient, must undergo water precorrection to linearize the measurement and to convert them into line integrals that are ready for reconstruction. The function to linearize the measured projection data depends on the detected spectrum of the ray. This spectrum may vary as a function of the detector position, e.g., in cases where the heel effect becomes relevant, where a bow-tie filter introduces channel-dependent beam hardening, or where a primary modulator is used to modulate the primary intensity of the spectrum. Methods: The authors propose a new approach that allows to handle these effects in a highly convenient way. Their new empirical cupping correction for primary modulation (ECCP) corrects for artifacts, such as cupping artifacts or ring artifacts, which are induced by nonlinearities in the projection data due to spatially varying pre- or postfiltration of the x-rays. To do so, ECCP requires only a simple scan of a homogeneous phantom of nearly arbitrary shape. Based on this information, coefficients of a polynomial series are calculated and stored for later use. Results: Physical measurements demonstrate the quality of the precorrection that can be achieved using ECCP to remove the cupping artifacts and to obtain well-calibrated CT values even in cases of strong primary modulation. A combination of ECCP with analytical techniques yielding a hybrid cupping correction method is possible and allows for channel-dependent correction functions. Conclusion: The proposed ECCP method is a very effective and easy to incorporate approach that compensates for even strong detector channel-dependent changes of the detected spectrum.

Metal artifacts drastically impair the image quality in CT images and often reduce their diagnostic value. There are many publications on metal artifact reduction (MAR) which regard the metal-affected parts of the rawdata as completely unreliable and therefore replace it. While those sinogram inpainting methods are in general successful in removing metal artifacts, they cannot exactly recover the true values which are replaced and therefore the corrected image will exhibit new artifacts and blurring.

Purpose: In the last decade, C-arm-based cone-beam CT became a widely used modality for intraoperative imaging. Typically a C-arm CT scan is performed using a circular or elliptical trajectory around a region of interest. Therefore, an angular range of at least 180° plus fan angle must be covered to ensure a completely sampled data set. However, mobile C-arms designed with a focus on classical 2D applications like fluoroscopy may be limited to a mechanical rotation range of less than 180° to improve handling and usability. The method proposed in this paper allows for the acquisition of a fully sampled data set with a system limited to a mechanical rotation range of at least 180° minus fan angle using a new trajectory design. This enables CT like 3D imaging with a wide range of C-arm devices which are mainly designed for 2D imaging. Methods: The proposed trajectory extends the mechanical rotation range of the C-arm system with two additional linear shifts. Due to the divergent character of the fan-beam geometry, these two shifts lead to an additional angular range of half of the fan angle. Combining one shift at the beginning of the scan followed by a rotation and a second shift, the resulting rotate-plus-shift trajectory enables the acquisition of a completely sampled data set using only 180° minus fan angle of rotation. The shifts can be performed using, e.g., the two orthogonal positioning axes of a fully motorized C-arm system. The trajectory was evaluated in phantom and cadaver examinations using two prototype C-arm systems. Results: The proposed trajectory leads to reconstructions without limited angle artifacts. Compared to the limited angle reconstructions of 180° minus fan angle, image quality increased dramatically. Details in the rotate-plus-shift reconstructions were clearly depicted, whereas they are dominated by artifacts in the limited angle scan. Conclusions: The method proposed here employs 3D imaging using C-arms with less than 180° rotation range adding full 3D functionality to a C-arm device retaining both handling comfort and the usability of 2D imaging. This method has a clear potential for clinical use especially to meet the increasing demand for an intraoperative 3D imaging.

4D magnetic resonance imaging (4DMRI) has a high potential for pancreatic cancer treatments using proton therapy, by providing time-resolved volumetric images with a high soft-tissue contrast without exposing the patient to any additional imaging dose. In this study, we aim to show the feasibility of 4D treatment planning for pencil beam scanning (PBS) proton therapy of pancreatic cancer, based on five repeated 4DMRI datasets and 4D dose calculations (4DDC) for one pancreatic cancer patient. To investigate the dosimetric impacts of organ motion, deformation vector fields were extracted from 4DMRI, which were then used to warp a static CT of the patient, so as to generate synthetic 4DCT (4DCT-MRI). CTV motion amplitudes <15 mm were observed for this patient. The results from 4DDC show pronounced interplay effects in the CTV with dose homogeneity d5/d95 and dose coverage v95 being 1.14 and 91%, respectively, after a single fraction of the treatment. An averaging effect was further observed when increasing the number of fractions. Motion effects can become less dominant and dose homogeneity d5/d95 = 1.03 and dose coverage v95 = [Formula: see text] within the CTV can be achieved after 28 fractions. The observed inter-fractional organ and tumor motion variations underline the importance of 4D imaging before and during PBS proton therapy.

Computed tomography (CT) has continued to attract public media attention from time to time [1]. Initially, during 2001–2007, it was the potential cancer risks in children with continuing reports intermittently; then unexpected overexposures in 2008–2009 that are unlikely to occur again; reports of over-utilization in 2007–2009 and reports of inappropriate use that keep appearing from time to time [1]. These reports during the past nearly two decades have heightened interest in making CT safer for patients.