Dariya I. Malyarenko

Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121.

Abstract Purpose: To determine quantitative quality control procedures to evaluate technical variability in multi‐center measurements of the diffusion coefficient of water as a prerequisite to use of the biomarker apparent diffusion coefficient (ADC) in multi‐center clinical trials. Materials and Methods: A uniform data acquisition protocol was developed and shared with 18 participating test sites along with a temperature‐controlled diffusion phantom delivered to each site. Usable diffusion weighted imaging data of ice water at five b‐values were collected on 35 clinical MRI systems from three vendors at two field strengths (1.5 and 3 Tesla [T]) and analyzed at a central processing site. Results: Standard deviation of bore‐center ADCs measured across 35 scanners was <2%; error range: −2% to +5% from literature value. Day‐to‐day repeatability of the measurements was within 4.5%. Intra‐exam repeatability at the phantom center was within 1%. Excluding one outlier, inter‐site reproducibility of ADC at magnet isocenter was within 3%, although variability increased for off‐center measurements. Significant (>10%) vendor‐specific and system‐specific spatial nonuniformity ADC bias was detected for the off‐center measurement that was consistent with gradient nonlinearity. Conclusion: Standardization of DWI protocol has improved reproducibility of ADC measurements and allowed identifying spatial ADC nonuniformity as a source of error in multi‐site clinical studies. J. Magn. Reson. Imaging 2013;37:1238–1246. © 2012 Wiley Periodicals, Inc.

Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as Ktrans (rate constant for plasma/interstitium contrast agent transfer), ve (extravascular extracellular volume fraction), and vp (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for Ktrans and vp being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the Ktrans intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for Ktrans) to 0.92 (for Ktrans percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor Ktrans and kep (= Ktrans/ve, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.

To determine the in vitro accuracy, test-retest repeatability, and interplatform reproducibility of T1 quantification protocols used for dynamic contrast-enhanced MRI at 1.5 and 3 T.A T1 phantom with 14 samples was imaged at eight centers with a common inversion-recovery spin-echo (IR-SE) protocol and a variable flip angle (VFA) protocol using seven flip angles, as well as site-specific protocols (VFA with different flip angles, variable repetition time, proton density, and Look-Locker inversion recovery). Factors influencing the accuracy (deviation from reference NMR T1 measurements) and repeatability were assessed using general linear mixed models. Interplatform reproducibility was assessed using coefficients of variation.For the common IR-SE protocol, accuracy (median error across platforms = 1.4-5.5%) was influenced predominantly by T1 sample (P < 10-6 ), whereas test-retest repeatability (median error = 0.2-8.3%) was influenced by the scanner (P < 10-6 ). For the common VFA protocol, accuracy (median error = 5.7-32.2%) was influenced by field strength (P = 0.006), whereas repeatability (median error = 0.7-25.8%) was influenced by the scanner (P < 0.0001). Interplatform reproducibility with the common VFA was lower at 3 T than 1.5 T (P = 0.004), and lower than that of the common IR-SE protocol (coefficient of variation 1.5T: VFA/IR-SE = 11.13%/8.21%, P = 0.028; 3 T: VFA/IR-SE = 22.87%/5.46%, P = 0.001). Among the site-specific protocols, Look-Locker inversion recovery and VFA (2-3 flip angles) protocols showed the best accuracy and repeatability (errors < 15%).The VFA protocols with 2 to 3 flip angles optimized for different applications achieved acceptable balance of extensive spatial coverage, accuracy, and repeatability in T1 quantification (errors < 15%). Further optimization in terms of flip-angle choice for each tissue application, and the use of B1 correction, are needed to improve the robustness of VFA protocols for T1 mapping. Magn Reson Med 79:2564-2575, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Background Proton density fat fraction (PDFF) estimated by using chemical shift-encoded (CSE) MRI is an accepted imaging biomarker of hepatic steatosis. This work aims to promote standardized use of CSE MRI to estimate PDFF. Purpose To assess the accuracy of CSE MRI methods for estimating PDFF by determining the linearity and range of bias observed in a phantom. Materials and Methods In this prospective study, a commercial phantom with 12 vials of known PDFF values were shipped across nine U.S. centers. The phantom underwent 160 independent MRI examinations on 27 1.5-T and 3.0-T systems from three vendors. Two three-dimensional CSE MRI protocols with minimal T1 bias were included: vendor and standardized. Each vendor's confounder-corrected complex or hybrid magnitude-complex based reconstruction algorithm was used to generate PDFF maps in both protocols. The Siemens reconstruction required a configuration change to correct for water-fat swaps in the phantom. The MRI PDFF values were compared with the known PDFF values by using linear regression with mixed-effects modeling. The 95% CIs were calculated for the regression slope (ie, proportional bias) and intercept (ie, constant bias) and compared with the null hypothesis (slope = 1, intercept = 0). Results Pooled regression slope for estimated PDFF values versus phantom-derived reference PDFF values was 0.97 (95% CI: 0.96, 0.98) in the biologically relevant 0%-47.5% PDFF range. The corresponding pooled intercept was -0.27% (95% CI: -0.50%, -0.05%). Across vendors, slope ranges were 0.86-1.02 (vendor protocols) and 0.97-1.0 (standardized protocol) at 1.5 T and 0.91-1.01 (vendor protocols) and 0.87-1.01 (standardized protocol) at 3.0 T. The intercept ranges (absolute PDFF percentage) were -0.65% to 0.18% (vendor protocols) and -0.69% to -0.17% (standardized protocol) at 1.5 T and -0.48% to 0.10% (vendor protocols) and -0.78% to -0.21% (standardized protocol) at 3.0 T. Conclusion Proton density fat fraction estimation derived from three-dimensional chemical shift-encoded MRI in a commercial phantom was accurate across vendors, imaging centers, and field strengths, with use of the vendors' product acquisition and reconstruction software. © RSNA, 2021 See also the editorial by Dyke in this issue.

Pharmacokinetic analysis of dynamic contrast-enhanced (DCE) MRI data allows estimation of quantitative imaging biomarkers such as Ktrans (rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical practice is limited with uncertainty in arterial input function (AIF) determination being one of the primary reasons. In this multicenter study to assess the effects of AIF variations on pharmacokinetic parameter estimation, DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Individual AIF from each data set was determined by each center and submitted to the managing center. These AIFs, along with a literature population averaged AIF, and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic data analysis using the Tofts model (TM). All other variables, including tumor region of interest (ROI) definition and pre-contrast T1, were kept constant to evaluate parameter variations caused solely by AIF discrepancies. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) of Ktrans obtained with unadjusted AIFs being as high as 0.74. AIF-caused variations were larger in Ktrans than ve and both were reduced when reference-tissue-adjusted AIFs were used. These variations were largely systematic, resulting in nearly unchanged parametric map patterns. The intravasation rate constant, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs: 0.45 vs. 0.74), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.

Purpose Characterize system‐specific bias across common magnetic resonance imaging (MRI) platforms for quantitative diffusion measurements in multicenter trials. Methods Diffusion weighted imaging (DWI) was performed on an ice‐water phantom along the superior–inferior (SI) and right–left (RL) orientations spanning ±150 mm. The same scanning protocol was implemented on 14 MRI systems at seven imaging centers. The bias was estimated as a deviation of measured from known apparent diffusion coefficient (ADC) along individual DWI directions. The relative contributions of gradient nonlinearity, shim errors, imaging gradients, and eddy currents were assessed independently. The observed bias errors were compared with numerical models. Results The measured systematic ADC errors scaled quadratically with offset from isocenter, and ranged between −55% (SI) and 25% (RL). Nonlinearity bias was dependent on system design and diffusion gradient direction. Consistent with numerical models, minor ADC errors (±5%) due to shim, imaging and eddy currents were mitigated by double echo DWI and image coregistration of individual gradient directions. Conclusion The analysis confirms gradient nonlinearity as a major source of spatial DW bias and variability in off‐center ADC measurements across MRI platforms, with minor contributions from shim, imaging gradients and eddy currents. The developed protocol enables empiric description of systematic bias in multicenter quantitative DWI studies. Magn Reson Med 75:1312–1323, 2016. © 2015 Wiley Periodicals, Inc.

Purpose To evaluate diffusion weighted MRI (DW-MR) as a response metric for assessment of neoadjuvant chemotherapy (NAC) in patients with primary breast cancer using prospective multi-center trials which provided MR scans along with clinical outcome information. Materials and Methods A total of 39 patients with locally advanced breast cancer accrued from three different prospective clinical trials underwent DW-MR examination prior to and at 3–7 days (Hull University), 8–11 days (University of Michigan) and 35 days (NeoCOMICE) post-treatment initiation. Thirteen patients, 12 of which participated in treatment response study, from UM underwent short interval (<1hr) MRI examinations, referred to as “test-retest” for examination of repeatability. To further evaluate stability in ADC measurements, a thermally controlled diffusion phantom was used to assess repeatability of diffusion measurements. MRI sequences included contrast-enhanced T1-weighted, when appropriate, and DW images acquired at b-values of 0 and 800 s/mm2. Histogram analysis and a voxel-based analytical technique, the Parametric Response Map (PRM), were used to derive diffusion response metrics for assessment of treatment response prediction. Results Mean tumor apparent diffusion coefficient (ADC) values generated from patient test-retest examinations were found to be very reproducible (|ΔADC|<0.1x10-3mm2/s). This data was used to calculate the 95% CI from the linear fit of tumor voxel ADC pairs of co-registered examinations (±0.45x10-3mm2/s) for PRM analysis of treatment response. Receiver operating characteristic analysis identified the PRM metric to be predictive of outcome at the 8–11 (AUC = 0.964, p = 0.01) and 35 day (AUC = 0.770, p = 0.05) time points (p<.05) while whole-tumor ADC changes where significant at the later 35 day time interval (AUC = 0.825, p = 0.02). Conclusion This study demonstrates the feasibility of performing a prospective analysis of DW-MRI as a predictive biomarker of NAC in breast cancer patients. In addition, we provide experimental evidence supporting the use of sensitive analytical tools, such as PRM, for evaluating ADC measurements.

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.

Background The Eastern Cooperative Oncology Group and American College of Radiology Imaging Network Cancer Research Group A6702 multicenter trial helped confirm the potential of diffusion-weighted MRI for improving differential diagnosis of suspicious breast abnormalities and reducing unnecessary biopsies. A prespecified secondary objective was to explore the relative value of different approaches for quantitative assessment of lesions at diffusion-weighted MRI. Purpose To determine whether alternate calculations of apparent diffusion coefficient (ADC) can help further improve diagnostic performance versus mean ADC values alone for analysis of suspicious breast lesions at MRI. Materials and Methods This prospective trial (ClinicalTrials.gov identifier: NCT02022579) enrolled consecutive women (from March 2014 to April 2015) with a Breast Imaging Reporting and Data System category of 3, 4, or 5 at breast MRI. All study participants underwent standardized diffusion-weighted MRI (b = 0, 100, 600, and 800 sec/mm2). Centralized ADC measures were performed, including manually drawn whole-lesion and hotspot regions of interest, histogram metrics, normalized ADC, and variable b-value combinations. Diagnostic performance was estimated by using the area under the receiver operating characteristic curve (AUC). Reduction in biopsy rate (maintaining 100% sensitivity) was estimated according to thresholds for each ADC metric. Results Among 107 enrolled women, 81 lesions with outcomes (28 malignant and 53 benign) in 67 women (median age, 49 years; interquartile range, 41-60 years) were analyzed. Among ADC metrics tested, none improved diagnostic performance versus standard mean ADC (AUC, 0.59-0.79 vs AUC, 0.75; P = .02-.84), and maximum ADC had worse performance (AUC, 0.52; P < .001). The 25th-percentile ADC metric provided the best performance (AUC, 0.79; 95% CI: 0.70, 0.88), and a threshold using median ADC provided the greatest reduction in biopsy rate of 23.9% (95% CI: 14.8, 32.9; 16 of 67 BI-RADS category 4 and 5 lesions). Nonzero minimum b value (100, 600, and 800 sec/mm2) did not improve the AUC (0.74; P = .28), and several combinations of two b values (0 and 600, 100 and 600, 0 and 800, and 100 and 800 sec/mm2; AUC, 0.73-0.76) provided results similar to those seen with calculations of four b values (AUC, 0.75; P = .17-.87). Conclusion Mean apparent diffusion coefficient calculated with a two-b-value acquisition is a simple and sufficient diffusion-weighted MRI metric to augment diagnostic performance of breast MRI compared with more complex approaches to apparent diffusion coefficient measurement. © RSNA, 2020 Online supplemental material is available for this article.

Abstract Background: Measurement of peptide/protein concentrations in biological samples for biomarker discovery commonly uses high-sensitivity mass spectrometers with a surface-processing procedure to concentrate the important peptides. These time-of-flight (TOF) instruments typically have low mass resolution and considerable electronic noise associated with their detectors. The net result is unnecessary overlapping of peaks, apparent mass jitter, and difficulty in distinguishing mass peaks from background noise. Many of these effects can be reduced by processing the signal using standard time-series background subtraction, calibration, and filtering techniques. Methods: Surface-enhanced laser desorption/ionization (SELDI) spectra were acquired on a PBS II instrument from blank, hydrophobic, and IMAC-Cu ProteinChip® arrays (Ciphergen Biosystems, Inc.) incubated with calibration peptide mixtures or pooled serum. TOF data were recorded after single and multiple laser shots at different positions. Correlative analysis was used for time-series calibration. Target filters were used to suppress noise and enhance resolution after baseline removal and noise rescaling. Results: The developed algorithms compensated for the electronic noise attributable to detector overload, removed the baseline caused by charge accumulation, detected and corrected mass peak jitter, enhanced signal amplitude at higher masses, and improved the resolution by using a deconvolution filter. Conclusions: These time-series techniques, when applied to SELDI-TOF data before any peak identification procedure, can improve the data to make the peak identification process simpler and more robust. These improvements may be applicable to most TOF instrumentation that uses analog (rather than counting) detectors.

Purpose Gradient nonlinearity of MRI systems leads to spatially dependent b ‐values and consequently high non‐uniformity errors (10–20%) in apparent diffusion coefficient (ADC) measurements over clinically relevant field‐of‐views. This work seeks practical correction procedure that effectively reduces observed ADC bias for media of arbitrary anisotropy in the fewest measurements. Methods All‐inclusive bias analysis considers spatial and time‐domain cross‐terms for diffusion and imaging gradients. The proposed correction is based on rotation of the gradient nonlinearity tensor into the diffusion gradient frame where spatial bias of b ‐matrix can be approximated by its Euclidean norm. Correction efficiency of the proposed procedure is numerically evaluated for a range of model diffusion tensor anisotropies and orientations. Results Spatial dependence of nonlinearity correction terms accounts for the bulk (75–95%) of ADC bias for FA = 0.3–0.9. Residual ADC non‐uniformity errors are amplified for anisotropic diffusion. This approximation obviates need for full diffusion tensor measurement and diagonalization to derive a corrected ADC. Practical scenarios are outlined for implementation of the correction on clinical MRI systems. Conclusions The proposed simplified correction algorithm appears sufficient to control ADC non‐uniformity errors in clinical studies using three orthogonal diffusion measurements. The most efficient reduction of ADC bias for anisotropic medium is achieved with non‐lab‐based diffusion gradients. Magn Reson Med 71:1312–1323, 2014. © 2013 Wiley Periodicals, Inc.

PURPOSE: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers. METHODS: A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create “variable signal,” whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity. RESULTS: Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images. CONCLUSION: Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.

Purpose To elucidate the dynamic, structural, and molecular properties that create inhomogeneous magnetization transfer (ihMT) contrast. Methods Amphiphilic lipids, lamellar phospholipids with cholesterol, and bovine spinal cord (BSC) specimens were examined along with nonlipid systems. Magnetization transfer (MT), enhanced MT (eMT, obtained with double‐sided radiofrequency saturation), ihMT (MT – eMT), and dipolar relaxation, T 1D , were measured at 2.0 and 11.7 T. Results The amplitude of ihMT ratio (ihMTR) is positively correlated with T 1D values. Both ihMTR and T 1D increase with increasing temperature in BSC white matter and in phospholipids and decrease with temperature in other lipids. Changes in ihMTR with temperature arise primarily from alterations in MT rather than eMT. Spectral width of MT, eMT, and ihMT increases with increasing carbon chain length. Conclusions Concerted motions of phospholipids in white matter decrease proton spin diffusion leading to increased proton T 1D times and increased ihMT amplitudes, consistent with decoupling of Zeeman and dipolar spin reservoirs. Molecular specificity and dynamic sensitivity of ihMT contrast make it a suitable candidate for probing myelin membrane disorders. Magn Reson Med 77:1318–1328, 2017. © 2016 International Society for Magnetic Resonance in Medicine

Renal cell carcinoma comprises 2 to 3% of malignancies in adults with the most prevalent subtype being clear-cell RCC (ccRCC). This type of cancer is well characterized at the genomic and transcriptomic level and is associated with a loss of VHL that results in stabilization of HIF1. The current study focused on evaluating ccRCC stage dependent changes at the proteome level to provide insight into the molecular pathogenesis of ccRCC progression. To accomplish this, label-free proteomics was used to characterize matched tumor and normal-adjacent tissues from 84 patients with stage I to IV ccRCC. Using pooled samples 1551 proteins were identified, of which 290 were differentially abundant, while 783 proteins were identified using individual samples, with 344 being differentially abundant. These 344 differentially abundant proteins were enriched in metabolic pathways and further examination revealed metabolic dysfunction consistent with the Warburg effect. Additionally, the protein data indicated activation of ESRRA and ESRRG, and HIF1A, as well as inhibition of FOXA1, MAPK1 and WISP2. A subset analysis of complementary gene expression array data on 47 pairs of these same tissues indicated similar upstream changes, such as increased HIF1A activation with stage, though ESRRA and ESRRG activation and FOXA1 inhibition were not predicted from the transcriptomic data. The activation of ESRRA and ESRRG implied that HIF2A may also be activated during later stages of ccRCC, which was confirmed in the transcriptional analysis. This combined analysis highlights the importance of HIF1A and HIF2A in developing the ccRCC molecular phenotype as well as the potential involvement of ESRRA and ESRRG in driving these changes. In addition, cofilin-1, profilin-1, nicotinamide N-methyltransferase, and fructose-bisphosphate aldolase A were identified as candidate markers of late stage ccRCC. Utilization of data collected from heterogeneous biological domains strengthened the findings from each domain, demonstrating the complementary nature of such an analysis. Together these results highlight the importance of the VHL/HIF1A/HIF2A axis and provide a foundation and therapeutic targets for future studies. (Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.)

Relevant to co-clinical trials, the goal of this work was to assess repeatability, reproducibility, and bias of the apparent diffusion coefficient (ADC) for preclinical MRIs using standardized procedures for comparison to performance of clinical MRIs. A temperature-controlled phantom provided an absolute reference standard to measure spatial uniformity of these performance metrics. Seven institutions participated in the study, wherein diffusion-weighted imaging (DWI) data were acquired over multiple days on 10 preclinical scanners, from 3 vendors, at 6 field strengths. Centralized versus site-based analysis was compared to illustrate incremental variance due to processing workflow. At magnet isocenter, short-term (intra-exam) and long-term (multiday) repeatability were excellent at within-system coefficient of variance, wCV [±CI] = 0.73% [0.54%, 1.12%] and 1.26% [0.94%, 1.89%], respectively. The cross-system reproducibility coefficient, RDC [±CI] = 0.188 [0.129, 0.343] µm2/ms, corresponded to 17% [12%, 31%] relative to the reference standard. Absolute bias at isocenter was low (within 4%) for 8 of 10 systems, whereas two high-bias (>10%) scanners were primary contributors to the relatively high RDC. Significant additional variance (>2%) due to site-specific analysis was observed for 2 of 10 systems. Base-level technical bias, repeatability, reproducibility, and spatial uniformity patterns were consistent with human MRIs (scaled for bore size). Well-calibrated preclinical MRI systems are capable of highly repeatable and reproducible ADC measurements.

Relative cerebral blood volume (rCBV) cannot be used as a response metric in clinical trials, in part, because of variations in biomarker consistency and associated interpretation across sites, stemming from differences in image acquisition and postprocessing methods (PMs). This study leveraged a dynamic susceptibility contrast magnetic resonance imaging digital reference object to characterize rCBV consistency across 12 sites participating in the Quantitative Imaging Network (QIN), specifically focusing on differences in site-specific imaging protocols (IPs; n = 17), and PMs (n = 19) and differences due to site-specific IPs and PMs (n = 25). Thus, high agreement across sites occurs when 1 managing center processes rCBV despite slight variations in the IP. This result is most likely supported by current initiatives to standardize IPs. However, marked intersite disagreement was observed when site-specific software was applied for rCBV measurements. This study's results have important implications for comparing rCBV values across sites and trials, where variability in PMs could confound the comparison of therapeutic effectiveness and/or any attempts to establish thresholds for categorical response to therapy. To overcome these challenges and ensure the successful use of rCBV as a clinical trial biomarker, we recommend the establishment of qualifying and validating site- and trial-specific criteria for scanners and acquisition methods (eg, using a validated phantom) and the software tools used for dynamic susceptibility contrast magnetic resonance imaging analysis (eg, using a digital reference object where the ground truth is known).

Noninvasive imaging methods are sought to objectively predict early response to therapy for high-grade glioma tumors. Quantitative metrics derived from diffusion-weighted imaging, such as apparent diffusion coefficient (ADC), have previously shown promise when used in combination with voxel-based analysis reflecting regional changes. The functional diffusion mapping (fDM) metric is hypothesized to be associated with volume of tumor exhibiting an increasing ADC owing to effective therapeutic action. In this work, the reference fDM-predicted survival (from previous study) for 3 weeks from treatment initiation (midtreatment) is compared to multiple histogram-based metrics using Kaplan–Meier estimator for 80 glioma patients stratified to responders and nonresponders based on the population median value for the given metric. The ADC histogram metric reflecting reduction in midtreatment volume of solid tumor (ADC &lt; 1.25 × 10−3 mm2/s) by &gt;8% population-median with respect to pretreatment is found to have the same predictive power as the reference fDM of increasing midtreatment ADC volume above 4%. This study establishes the level of correlation between fDM increase and low-ADC tumor volume shrinkage for prediction of early response to radiation therapy in patients with glioma malignancies.

Uncertainty regarding the reproducibility of the apparent diffusion coefficient (ADC) hampers the use of quantitative diffusion-weighted imaging (DWI) in evaluation of the prostate with magnetic resonance imaging MRI. The quantitative imaging biomarkers alliance (QIBA) profile for quantitative DWI claims a within-subject coefficient of variation (wCV) for prostate lesion ADC of 0.17. Improved understanding of ADC reproducibility would aid the use of quantitative diffusion in prostate MRI evaluation.Evaluation of the repeatability (same-day) and reproducibility (multi-day) of whole-prostate and focal-lesion ADC assessment in a multi-site setting.Prospective multi-institutional.Twenty-nine males, ages 53 to 80 (median 63) years, following diagnosis of prostate cancer, 10 with focal lesions.3T, single-shot spin-echo diffusion-weighted echo-planar sequence with four b-values.Sites qualified for the study using an ice-water phantom with known ADC. Readers performed DWI analyses at visit 1 ("V1") and visit 2 ("V2," 2-14 days after V1), where V2 comprised scans before ("V2pre") and after ("V2post") a "coffee-break" interval with subject removal and repositioning. A single reader segmented the whole prostate. Two readers separately placed region-of-interests for focal lesions.Reproducibility and repeatability coefficients for whole prostate and focal lesions derived from median pixel ADC. We estimated the wCV and 95% confidence interval using a variance stabilizing transformation and assessed interreader reliability of focal lesion ADC using the intraclass correlation coefficient (ICC).The ADC biases from b0 -b600 and b0 -b800 phantom scans averaged 1.32% and 1.44%, respectively; mean b-value dependence was 0.188%. Repeatability and reproducibility of whole prostate median pixel ADC both yielded wCVs of 0.033 (N = 29). In 10 subjects with an evaluable focal lesion, the individual reader wCVs were 0.148 and 0.074 (repeatability) and 0.137 and 0.078 (reproducibility). All time points demonstrated good to excellent interreader reliability for focal lesion ADC (ICCV1 = 0.89; ICCV2pre = 0.76; ICCV2post = 0.94).This study met the QIBA claim for prostate ADC. Test-retest repeatability and multi-day reproducibility were largely equivalent. Interreader reliability for focal lesion ADC was high across time points.1 TECHNICAL EFFICACY: Stage 2 TOC CATEGORY: Pelvis.

The chemical bonding of hard and elastic amorphous carbon nitride $(a\ensuremath{-}{\mathrm{CN}}_{x})$ thin films was examined using solid-state ${}^{13}\mathrm{C}$ NMR spectroscopy. The films were deposited by DC magnetron sputtering in a pure nitrogen discharge on Si(001) substrates at 300 \ifmmode^\circ\else\textdegree\fi{}C. Nanoindentation tests reveal a recovery of 80%, a hardness of 5 GPa, and an elastic modulus of 47 GPa. This combination of low modulus and high strength means the material can be regarded as hard and elastic; the material gives when pressed on and recovers its shape when the load is released. The ${}^{13}\mathrm{C}$ NMR results conclusively demonstrate that hard and elastic $a\ensuremath{-}{\mathrm{CN}}_{x}$ has an ${\mathrm{sp}}^{2}$ carbon bonded structure and that ${\mathrm{sp}}^{3}$ hybridized carbons are absent. Our results stand in contrast with earlier work that proposed that the interesting mechanical properties of hard and elastic $a\ensuremath{-}{\mathrm{CN}}_{x}$ were due, in part, to ${\mathrm{sp}}^{3}$ bonded carbon.

Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two (ADC2) and four (ADC4) b-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo ADC2, with relative biases <0.1 % (ADC2) and <0.5 % (phantom ADC4) but with higher deviations in ADC at the lowest phantom ADC values. In vivo ADC4 concordance was good, with typical biases of ±2 % to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for ADC4in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.

The aim of this study was to establish the repeatability measures of quantitative Gaussian and non-Gaussian diffusion metrics using diffusion-weighted imaging (DWI) data from phantoms and patients with head-and-neck and papillary thyroid cancers. The Quantitative Imaging Biomarker Alliance (QIBA) DWI phantom and a novel isotropic diffusion kurtosis imaging phantom were scanned at 3 different sites, on 1.5T and 3T magnetic resonance imaging systems, using standardized multiple b-value DWI acquisition protocol. In the clinical component of this study, a total of 60 multiple b-value DWI data sets were analyzed for test-retest, obtained from 14 patients (9 head-and-neck squamous cell carcinoma and 5 papillary thyroid cancers). Repeatability of quantitative DWI measurements was assessed by within-subject coefficient of variation (wCV%) and Bland-Altman analysis. In isotropic diffusion kurtosis imaging phantom vial with 2% ceteryl alcohol and behentrimonium chloride solution, the mean apparent diffusion (Dapp × 10-3 mm2/s) and kurtosis (Kapp, unitless) coefficient values were 1.02 and 1.68 respectively, capturing in vivo tumor cellularity and tissue microstructure. For the same vial, Dapp and Kapp mean wCVs (%) were ≤1.41% and ≤0.43% for 1.5T and 3T across 3 sites. For pretreatment head-and-neck squamous cell carcinoma, apparent diffusion coefficient, D, D*, K, and f mean wCVs (%) were 2.38%, 3.55%, 3.88%, 8.0%, and 9.92%, respectively; wCVs exhibited a higher trend for papillary thyroid cancers. Knowledge of technical precision and bias of quantitative imaging metrics enables investigators to properly design and power clinical trials and better discern between measurement variability versus biological change.

Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.

Purpose To describe an efficient procedure to empirically characterize gradient nonlinearity and correct for the corresponding apparent diffusion coefficient (ADC) bias on a clinical magnetic resonance imaging (MRI) scanner. Materials and Methods Spatial nonlinearity scalars for individual gradient coils along superior and right directions were estimated via diffusion measurements of an isotropic ice‐water phantom. Digital nonlinearity model from an independent scanner, described in the literature, was rescaled by system‐specific scalars to approximate 3D bias correction maps. Correction efficacy was assessed by comparison to unbiased ADC values measured at isocenter. Results Empirically estimated nonlinearity scalars were confirmed by geometric distortion measurements of a regular grid phantom. The applied nonlinearity correction for arbitrarily oriented diffusion gradients reduced ADC bias from ∼20% down to ∼2% at clinically relevant offsets both for isotropic and anisotropic media. Identical performance was achieved using either corrected diffusion‐weighted imaging (DWI) intensities or corrected b ‐values for each direction in brain and ice‐water. Direction‐average trace image correction was adequate only for isotropic medium. Conclusion Empiric scalar adjustment of an independent gradient nonlinearity model adequately described DWI bias for a clinical scanner. Observed efficiency of implemented ADC bias correction quantitatively agreed with previous theoretical predictions and numerical simulations. The described procedure provides an independent benchmark for nonlinearity bias correction of clinical MRI scanners. J. Magn. Reson. Imaging 2014;40:1487–1495 . © 2013 Wiley Periodicals, Inc .

Rationale and Objectives Magnetic resonance diffusion imaging can characterize physiologic characteristics of pediatric brain tumors used to assess therapy response. The purpose of this study was to assess the variability of the apparent diffusion coefficient (ADC) along z-axis of scanners in the multicenter Pediatric Brain Tumor Consortium (PBTC). Materials and Methods Ice–water diffusion phantoms for each PBTC site were distributed with a specific diffusion imaging protocol. The phantom was scanned four successive times to 1) confirm water in the tube reached thermal equilibrium and 2) allow for assessment of intra-examination ADC repeatability. ADC profiles across slice positions for each vendor and institution combination were characterized using linear regression modeling with a quadratic fit. Results Eleven sites collected data with a high degree of compliance to the diffusion protocol for each scanner. The mean ADC value at slice position zero for vendor A was 1.123 × 10−3 mm2/s, vendor B was 1.0964 × 10−3 mm2/s, and vendor C was 1.110 × 10−3 mm2/s. The percentage coefficient of variation across all sites was 0.309% (standard deviation = 0.322). The ADC values conformed well to a second-order polynomial along the z-axis, (ie, following a linear model pattern with quadratic fit) for vendor–institution combinations and across vendor–institution combinations as shown in the longitudinal model. Conclusions Assessment of the variability of diffusion metrics is essential for establishing the validity of using these quantitative metrics in multicenter trials. The low variability in ADC values across vendors and institutions and validates the use of ADC as a quantitative tumor marker in pediatric multicenter trials. Magnetic resonance diffusion imaging can characterize physiologic characteristics of pediatric brain tumors used to assess therapy response. The purpose of this study was to assess the variability of the apparent diffusion coefficient (ADC) along z-axis of scanners in the multicenter Pediatric Brain Tumor Consortium (PBTC). Ice–water diffusion phantoms for each PBTC site were distributed with a specific diffusion imaging protocol. The phantom was scanned four successive times to 1) confirm water in the tube reached thermal equilibrium and 2) allow for assessment of intra-examination ADC repeatability. ADC profiles across slice positions for each vendor and institution combination were characterized using linear regression modeling with a quadratic fit. Eleven sites collected data with a high degree of compliance to the diffusion protocol for each scanner. The mean ADC value at slice position zero for vendor A was 1.123 × 10−3 mm2/s, vendor B was 1.0964 × 10−3 mm2/s, and vendor C was 1.110 × 10−3 mm2/s. The percentage coefficient of variation across all sites was 0.309% (standard deviation = 0.322). The ADC values conformed well to a second-order polynomial along the z-axis, (ie, following a linear model pattern with quadratic fit) for vendor–institution combinations and across vendor–institution combinations as shown in the longitudinal model. Assessment of the variability of diffusion metrics is essential for establishing the validity of using these quantitative metrics in multicenter trials. The low variability in ADC values across vendors and institutions and validates the use of ADC as a quantitative tumor marker in pediatric multicenter trials.

Abstract We have developed an automated procedure for aligning peaks in multiple TOF spectra that eliminates common timing errors and small variations in spectrometer output. Our method incorporates high‐resolution peak detection, re‐binning, and robust linear data fitting in the time domain. This procedure aligns label‐free (uncalibrated) peaks to minimize the variation in each peak's location from one spectrum to the next, while maintaining a high number of degrees of freedom. We apply our method to replicate pooled‐serum spectra from multiple laboratories and increase peak precision ( t / σ t ) to values limited only by small random errors (with σ t less than one time count in 89 out of 91 instances, 13 peaks in seven datasets). The resulting high precision allowed for an order of magnitude improvement in peak m / z reproducibility. We show that the CV for m / z is 0.01% (100 ppm) for 12 out of the 13 peaks that were observed in all datasets between 2995 and 9297 Da.

Background Diffusion‐weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease. Purpose To compare 14 site‐specific parametric fitting implementations applied to the same dataset of whole‐mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms. Study Type Prospective. Population Thirty‐three patients prospectively imaged prior to prostatectomy. Field Strength/Sequence 3 T, field‐of‐view optimized and constrained undistorted single‐shot DWI sequence. Assessment Datasets, including a noise‐free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono‐exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi‐exponential diffusion (BID), pseudo‐diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC). Statistical Test Levene's test, P &lt; 0.05 corrected for multiple comparisons was considered statistically significant. Results The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72–0.76, 0.76–0.81, and 0.76–0.80 respectively) as compared to bi‐exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53–0.80, 0.51–0.81, and 0.52–0.80 respectively). Post‐processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size. Data Conclusion We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post‐processing decisions on DWI data can affect sensitivity and specificity when applied to radiological–pathological studies in prostate cancer. Level of Evidence 1 Technical Efficacy Stage 3

Purpose To assess the reliability of measuring diffusivity, diffusional kurtosis, and cellular‐interstitial water exchange time with long diffusion times (100–800 ms) using stimulated‐echo DWI. Methods Time‐dependent diffusion MRI was tested on two well‐established diffusion phantoms and in 5 patients with head and neck cancer. Measurements were conducted using an in‐house diffusion‐weighted STEAM‐EPI pulse sequence with multiple diffusion times at a fixed TE on three scanners. We used the weighted linear least‐squares fit method to estimate time‐dependent diffusivity, , and diffusional kurtosis, . Additionally, the Kärger model was used to estimate cellular‐interstitial water exchange time () from . Results Diffusivity measured by time‐dependent STEAM‐EPI measurements and commercial SE‐EPI showed comparable results with R 2 above 0.98 and overall 5.4 ± 3.0% deviation across diffusion times. Diffusional kurtosis phantom data showed expected patterns: constant and = 0 for negative controls and slow varying and for samples made of nanoscopic vesicles. Time‐dependent diffusion MRI in patients with head and neck cancer found that the Kärger model could be considered valid in 72% ± 23% of the voxels in the metastatic lymph nodes. The median cellular‐interstitial water exchange time estimated for lesions was between 58.5 ms and 70.6 ms. Conclusions Based on two well‐established diffusion phantoms, we found that time‐dependent diffusion MRI measurements can provide stable diffusion and kurtosis values over a wide range of diffusion times and across multiple MRI systems. Moreover, estimation of cellular‐interstitial water exchange time can be achieved using the Kärger model for the metastatic lymph nodes in patients with head and neck cancer.

Quantitative MRI biomarkers are sought to replace painful and invasive sequential bone-marrow biopsies routinely used for myelofibrosis (MF) cancer monitoring and treatment assessment. Repeatability of MRI-based quantitative imaging biomarker (QIB) measurements was investigated for apparent diffusion coefficient (ADC), proton density fat fraction (PDFF), and magnetization transfer ratio (MTR) in a JAK2 V617F hematopoietic transplant model of MF. Repeatability coefficients (RCs) were determined for three defined tibia bone-marrow sections (2-9 mm; 10-12 mm; and 12.5-13.5 mm from the knee joint) across 15 diseased mice from 20-37 test-retest pairs. Scans were performed on consecutive days every two weeks for a period of 10 weeks starting 3-4 weeks after transplant. The mean RC with (95% confidence interval (CI)) for these sections, respectively, were for ADC: 0.037 (0.031, 0.050), 0.087 (0.069, 0.116), and 0.030 (0.022, 0.044) μm2/ms; for PDFF: 1.6 (1.3, 2.0), 15.5 (12.5, 20.2), and 25.5 (12.0, 33.0)%; and for MTR: 0.16 (0.14, 0.19), 0.11 (0.09, 0.15), and 0.09 (0.08, 0.15). Change-trend analysis of these QIBs identified a dynamic section within the mid-tibial bone marrow in which confident changes (exceeding RC) could be observed after a four-week interval between scans across all measured MRI-based QIBs. Our results demonstrate the capability to derive quantitative imaging metrics from mouse tibia bone marrow for monitoring significant longitudinal MF changes.

A murine model of myelofibrosis in tibia was used in a co-clinical trial to evaluate segmentation methods for application of image-based biomarkers to assess disease status. The dataset (32 mice with 157 3D MRI scans including 49 test-retest pairs scanned on consecutive days) was split into approximately 70% training, 10% validation, and 20% test subsets. Two expert annotators (EA1 and EA2) performed manual segmentations of the mouse tibia (EA1: all data; EA2: test and validation). Attention U-net (A-U-net) model performance was assessed for accuracy with respect to EA1 reference using the average Jaccard index (AJI), volume intersection ratio (AVI), volume error (AVE), and Hausdorff distance (AHD) for four training scenarios: full training, two half-splits, and a single-mouse subsets. The repeatability of computer versus expert segmentations for tibia volume of test-retest pairs was assessed by within-subject coefficient of variance (%wCV). A-U-net models trained on full and half-split training sets achieved similar average accuracy (with respect to EA1 annotations) for test set: AJI = 83-84%, AVI = 89-90%, AVE = 2-3%, and AHD = 0.5 mm-0.7 mm, exceeding EA2 accuracy: AJ = 81%, AVI = 83%, AVE = 14%, and AHD = 0.3 mm. The A-U-net model repeatability wCV [95% CI]: 3 [2, 5]% was notably better than that of expert annotators EA1: 5 [4, 9]% and EA2: 8 [6, 13]%. The developed deep learning model effectively automates murine bone marrow segmentation with accuracy comparable to human annotators and substantially improved repeatability.

Providing method descriptions that are more detailed than currently available in typical peer reviewed journals has been identified as an actionable area for improvement. In the biochemical and cell biology space, this need has been met through the creation of new journals focused on detailed protocols and materials sourcing. However, this format is not well suited for capturing instrument validation, detailed imaging protocols, and extensive statistical analysis. Furthermore, the need for additional information must be counterbalanced by the additional time burden placed upon researchers who may be already overtasked. To address these competing issues, this white paper describes protocol templates for positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance imaging (MRI) that can be leveraged by the broad community of quantitative imaging experts to write and self-publish protocols in protocols.io. Similar to the Structured Transparent Accessible Reproducible (STAR) or Journal of Visualized Experiments (JoVE) articles, authors are encouraged to publish peer reviewed papers and then to submit more detailed experimental protocols using this template to the online resource. Such protocols should be easy to use, readily accessible, readily searchable, considered open access, enable community feedback, editable, and citable by the author.

Analysis of complex biological samples by MALDI-TOF mass spectrometry has been generally limited to the detection of low-mass protein (or protein fragment) peaks. We have extended the mass range of MALDI-TOF high-sensitivity detection by an order of magnitude through the combined optimization of instrument parameters, data processing, and sample preparation procedures for affinity capture. WCX, C3, and IMAC magnetic beads were determined to be complementary and most favorable for broad mass range protein profiling. Key instrument parameters for extending mass range included adjustment of the ADC offset and preamplifier filter values of the TOF detector. Data processing was improved by a combination of constant and quadratic down-sampling, preceded by exponential baseline subtraction, to increase sensitivity of signal peaks. This enhancement in broad mass range detection of protein signals will be of direct benefit in MS expression profiling studies requiring full linear range mass detection.

Time-of-flight mass spectrometry (TOF-MS) has the potential to provide non-invasive, high-throughput screening for cancers and other serious diseases via detection of protein biomarkers in blood or other accessible biologic samples. Unfortunately, this potential has largely been unrealized to date due to the high variability of measurements, uncertainties in the distribution of proteins in a given population, and the difficulty of extracting repeatable diagnostic markers using current statistical tools. With studies consisting of perhaps only dozens of samples, and possibly hundreds of variables, overfitting is a serious complication. To overcome these difficulties, we have developed a Bayesian inductive method which uses model-independent methods of discovering relationships between spectral features. This method appears to efficiently discover network models which not only identify connections between the disease and key features, but also organizes relationships between features--and furthermore creates a stable classifier that categorizes new data at predicted error rates. The method was applied to artificial data with known feature relationships and typical TOF-MS variability introduced, and was able to recover those relationships nearly perfectly. It was also applied to blood sera data from a 2004 leukemia study, and showed high stability of selected features under cross-validation. Verification of results using withheld data showed excellent predictive power. The method showed improvement over traditional techniques, and naturally incorporated measurement uncertainties. The relationships discovered between features allowed preliminary identification of a protein biomarker which was consistent with other cancer studies and later verified experimentally. This method appears to avoid overfitting in biologic data and produce stable feature sets in a network model. The network structure provides additional information about the relationships among features that is useful to guide further biochemical analysis. In addition, when used to classify new data, these feature sets are far more consistent than those produced by many traditional techniques.

This study examines the relationship between quantitative magnetization transfer (qMT) parameters and the molecular composition of a model lamellar liquid crystal (LLC) system composed of 1-decyl alcohol (decanol), sodium dodecyl sulfate (SDS), and water.Samples were made within a stable lamellar mesophase to provide different ratios of total semisolid protons (SDS + decanol) to water protons. Data were collected as a function of radiofrequency power, frequency offset, and temperature. qMT parameters were estimated by fitting a standard model to the data. Fitting results of four different semisolid line shapes were compared.A super-Lorentzian line shape for the semisolid component provided the best fit. The estimated amount of semisolids was proportional to the ratio of decanol-to-water protons. Other qMT parameters exhibited nonlinear dependence on sample composition. Magnetization transfer ratio (MTR) was a linear function of the semisolid fraction over a limited range of decanol concentration.In LLC samples, MT between semisolid and water originates from intramolecular nOe among decanol aliphatic chain protons followed by proton exchange between decanol hydroxyl and water. Exchange kinetics is influenced by SDS, although SDS protons do not participate in MT. These studies provide clinically relevant range of semisolid fraction proportional to detected MTR.

Purpose Chemical shift‐encoded MRI (CSE‐MRI) is well‐established to quantify proton density fat fraction (PDFF) as a quantitative biomarker of hepatic steatosis. However, temperature is known to bias PDFF estimation in phantom studies. In this study, strategies were developed and evaluated to correct for the effects of temperature on PDFF estimation through simulations, temperature‐controlled experiments, and a multi‐center, multi‐vendor phantom study. Theory and Methods A technical solution that assumes and automatically estimates a uniform, global temperature throughout the phantom is proposed. Computer simulations modeled the effect of temperature on PDFF estimation using magnitude‐, complex‐, and hybrid‐based CSE‐MRI methods. Phantom experiments were performed to assess the temperature correction on PDFF estimation at controlled phantom temperatures. To assess the temperature correction method on a larger scale, the proposed method was applied to data acquired as part of a nine‐site multi‐vendor phantom study and compared to temperature‐corrected PDFF estimation using an a priori guess for ambient room temperature. Results Simulations and temperature‐controlled experiments show that as temperature deviates further from the assumed temperature, PDFF bias increases. Using the proposed correction method and a reasonable a priori guess for ambient temperature, PDFF bias and variability were reduced using magnitude‐based CSE‐MRI, across MRI systems, field strengths, protocols, and varying phantom temperature. Complex and hybrid methods showed little PDFF bias and variability both before and after correction. Conclusion Correction for temperature reduces temperature‐related PDFF bias and variability in phantoms across MRI vendors, sites, field strengths, and protocols for magnitude‐based CSE‐MRI, even without a priori information about the temperature.

Histopathology, the standard method to assess BM in hematologic malignancies such as myeloproliferative neoplasms (MPNs), suffers from notable limitations in both research and clinical settings. BM biopsies in patients fail to detect disease heterogeneity, may yield a nondiagnostic sample, and cannot be repeated frequently in clinical oncology. Endpoint histopathology precludes monitoring disease progression and response to therapy in the same mouse over time, missing likely variations among mice. To overcome these shortcomings, we used MRI to measure changes in cellularity, macromolecular constituents, and fat versus hematopoietic cells in BM using diffusion-weighted imaging (DWI), magnetization transfer, and chemical shift-encoded fat imaging. Combining metrics from these imaging parameters revealed dynamic alterations in BM following myeloablative radiation and transplantation. In a mouse MPLW515L BM transplant model of MPN, MRI detected effects of a JAK2 inhibitor, ruxolitinib, within 5 days of initiating treatment and identified differing kinetics of treatment responses in subregions of the tibia. Histopathology validated the MRI results for BM composition and heterogeneity. Anatomic MRI scans also showed reductions in spleen volume during treatment. These findings establish an innovative, clinically translatable MRI approach to quantify spatial and temporal changes in BM in MPN.

Previous research has shown that system-dependent gradient nonlinearity (GNL) introduces a significant spatial bias (nonuniformity) in apparent diffusion coefficient (ADC) maps. Here, the feasibility of centralized retrospective system-specific correction of GNL bias for quantitative diffusion-weighted imaging (DWI) in multisite clinical trials is demonstrated across diverse scanners independent of the scanned object. Using corrector maps generated from system characterization by ice-water phantom measurement completed in the previous project phase, GNL bias correction was performed for test ADC measurements from an independent DWI phantom (room temperature agar) at two offset locations in the bore. The precomputed three-dimensional GNL correctors were retrospectively applied to test DWI scans by the central analysis site. The correction was blinded to reference DWI of the agar phantom at magnet isocenter where the GNL bias is negligible. The performance was evaluated from changes in ADC region of interest histogram statistics before and after correction with respect to the unbiased reference ADC values provided by sites. Both absolute error and nonuniformity of the ADC map induced by GNL (median, 12%; range, -35% to +10%) were substantially reduced by correction (7-fold in median and 3-fold in range). The residual ADC nonuniformity errors were attributed to measurement noise and other non-GNL sources. Correction of systematic GNL bias resulted in a 2-fold decrease in technical variability across scanners (down to site temperature range). The described validation of GNL bias correction marks progress toward implementation of this technology in multicenter trials that utilize quantitative DWI.

Quantitative kurtosis phantoms are sought by multicenter clinical trials to establish accuracy and precision of quantitative imaging biomarkers on the basis of diffusion kurtosis imaging (DKI) parameters. We designed and evaluated precision, reproducibility, and long-term stability of a novel isotropic (i) DKI phantom fabricated using four families of chemicals based on vesicular and lamellar mesophases of liquid crystal materials. The constructed iDKI phantoms included negative control monoexponential diffusion materials to independently characterize noise and model-induced bias in quantitative kurtosis parameters. Ten test–retest DKI studies were performed on four scanners at three imaging centers over a six-month period. The tested prototype phantoms exhibited physiologically relevant apparent diffusion, Dapp, and kurtosis, Kapp, parameters ranging between 0.4 and 1.1 (×10−3 mm2/s) and 0.8 and 1.7 (unitless), respectively. Measured kurtosis phantom Kapp exceeded maximum fit model bias (0.1) detected for negative control (zero kurtosis) materials. The material-specific parameter precision [95% CI for Dapp: 0.013–0.022(×10−3 mm2/s) and for Kapp: 0.009–0.076] derived from the test–retest analysis was sufficient to characterize thermal and temporal stability of the prototype DKI phantom through correlation analysis of inter-scan variability. The present study confirms a promising chemical design for stable quantitative DKI phantom based on vesicular mesophase of liquid crystal materials. Improvements to phantom preparation and temperature monitoring procedures have potential to enhance precision and reproducibility for future multicenter iDKI phantom studies.

Purpose Anisotropic transverse R 2 (1/ T 2 ) relaxation of water proton is sensitive to cartilage degenerative changes. The purpose is to develop an efficient method to extract this relaxation metric in clinical studies. Methods Anisotropic R 2 can be measured inefficiently by standard R 2 mapping after removing an isotropic contribution obtained from R 1 ρ mapping. In the proposed method, named as a unique anisotropic R 2 of collagen degeneration (ARCADE) mapping, an assumed uniform isotropic R 2 was estimated at magic angle locations in the deep cartilage, and an anisotropic R 2 was thus isolated in a single T2W sagittal image. Five human knees from 4 volunteers were studied with standard R 2 and R 1 ρ mappings at 3T, and anisotropic R 2 derived from ARCADE on the T2W (TE = 48.8 ms) image from R 2 mapping was compared with the composite relaxation ( R 2 – R 1 ρ ) using statistical analysis including Student's t‐test and Pearson’s correlation coefficient. Results Anisotropic R 2 (1/s) from ARCADE was highly positively correlated with but not significantly different from standard R 2 – R 1 ρ (1/s) in the segmented deep ( r = 0.83 ± 0.06; 8.3 ± 2.9 vs. 7.3 ± 1.9, P = .50) and the superficial ( r = 0.82 ± 0.05; 3.5 ± 2.4 vs. 4.5 ± 1.6, P = .39) zones. However, after eliminating systematic errors by the normalization in terms of zonal contrast, anisotropic R 2 was significantly higher (60.2 ± 18.5% vs. 38.4 ± 16.6%, P &lt; .01) than R 2 – R 1 ρ as predicted. Conclusion The proposed anisotropic R 2 mapping could be an efficient alternative to the conventional approach, holding great promise in providing both high‐resolution morphological and more sensitive transverse relaxation imaging from a single T2W scan in a clinical setting.

We have previously characterized the reproducibility of brain tumor relative cerebral blood volume (rCBV) using a dynamic susceptibility contrast magnetic resonance imaging digital reference object across 12 sites using a range of imaging protocols and software platforms. As expected, reproducibility was highest when imaging protocols and software were consistent, but decreased when they were variable. Our goal in this study was to determine the impact of rCBV reproducibility for tumor grade and treatment response classification. We found that varying imaging protocols and software platforms produced a range of optimal thresholds for both tumor grading and treatment response, but the performance of these thresholds was similar. These findings further underscore the importance of standardizing acquisition and analysis protocols across sites and software benchmarking.

The goal of this work is to provide temperature and concentration calibration of water diffusivity in polyvinylpyrrolidone (PVP) solutions used in phantoms to assess system bias and linearity in apparent diffusion coefficient (ADC) measurements.ADC measurements were performed for 40 kDa (K40) PVP of six concentrations (0%, 10%, 20%, 30%, 40%, and 50% by weight) at three temperatures (19.5°C, 22.5°C, and 26.4°C), with internal phantom temperature monitored by optical thermometer (±0.2°C). To achieve ADC measurement and fit accuracy of better than 0.5%, three orthogonal diffusion gradients were calibrated using known water diffusivity at 0°C and system gradient nonlinearity maps. Noise-floor fit bias was also controlled by limiting the maximum b-value used for ADC calculation of each sample. The ADC temperature dependence was modeled by Arrhenius functions of each PVP concentration. The concentration dependence was modeled by quadratic function for ADC normalized by the theoretical water diffusion values. Calibration coefficients were obtained from linear regression model fits.Measured phantom ADC values increased with temperature and decreasing PVP concentration, [PVP]. The derived Arrhenius model parameters for [PVP] between 0% and 50%, are reported and can be used for K40 ADC temperature calibration with absolute ADC error within ±0.016 μm2 /ms. Arrhenius model fit parameters normalized to water value scaled with [PVP] between 10% and 40%, and proportional change in activation energy increased faster than collision frequency. ADC normalization by water diffusivity, DW , from the Speedy-Angell relation accounted for the bulk of temperature dependence (±0.035 μm2 /ms) and yielded quadratic calibration for ADCPVP /DW = (12.5 ± 0.7) ·10-5 ·[PVP]2 - (23.2 ± 0.3)·10-3 ·[PVP]+1, nearly independent of PVP molecular weight and temperature.The study provides ground-truth ADC values for K40 PVP solutions commonly used in diffusion phantoms for scanning at ambient room temperature. The described procedures and the reported calibration can be used for quality control and standardization of measured ADC values of PVP at different concentrations and temperatures.

Effect of Gadoxetate Disodium on Arterial Phase Respiratory Waveforms Using a Quantitative Fast Fourier Transformation–Based AnalysisMatthew S. Davenport1,2,3, Dariya I. Malyarenko1, Yuxi Pang1, Hero K. Hussain1 and Thomas L. Chenevert1Audio Available | Share

Mean tumor apparent diffusion coefficient (ADC) of breast cancer showed excellent repeatability but only moderate predictive power for breast cancer therapy response in the ACRIN 6698 multicenter imaging trial. Previous single-center studies have shown improved predictive performance for alternative ADC histogram metrics related to low ADC dense tumor volume. Using test/retest (TT/RT) 4 b-value diffusion-weighted imaging acquisitions from pretreatment or early-treatment time-points on 71 ACRIN 6698 patients, we evaluated repeatability for ADC histogram metrics to establish confidence intervals and inform predictive models for future therapy response analysis. Histograms were generated using regions of interest (ROIs) defined separately for TT and RT diffusion-weighted imaging. TT/RT repeatability and intra- and inter-reader reproducibility (on a 20-patient subset) were evaluated using wCV and Bland–Altman limits of agreement for histogram percentiles, low-ADC dense tumor volumes, and fractional volumes (normalized to total histogram volume). Pearson correlation was used to reveal connections between metrics and ROI variability across the sample cohort. Low percentiles (15th and 25th) were highly repeatable and reproducible, wCV &lt; 8.1%, comparable to mean ADC values previously reported. Volumetric metrics had higher wCV values in all cases, with fractional volumes somewhat better but at least 3 times higher than percentile wCVs. These metrics appear most sensitive to ADC changes around a threshold of 1.2 μm2/ms. Volumetric results were moderately to strongly correlated with ROI size. In conclusion, Lower histogram percentiles have comparable repeatability to mean ADC, while ADC-thresholded volumetric measures currently have poor repeatability but may benefit from improvements in ROI techniques.

The A6702 multisite trial confirmed that apparent diffusion coefficient (ADC) measures can improve breast MRI accuracy and reduce unnecessary biopsies, but also found that technical issues rendered many lesions non-evaluable on diffusion-weighted imaging (DWI). This secondary analysis investigated factors affecting lesion evaluability and impact on diagnostic performance.The A6702 protocol was IRB-approved at 10 institutions; participants provided informed consent. In total, 103 women with 142 MRI-detected breast lesions (BI-RADS assessment category 3, 4, or 5) completed the study. DWI was acquired at 1.5T and 3T using a four b-value, echo-planar imaging sequence. Scans were reviewed for multiple quality factors (artifacts, signal-to-noise, misregistration, and fat suppression); lesions were considered non-evaluable if there was low confidence in ADC measurement. Associations of lesion evaluability with imaging and lesion characteristics were determined. Areas under the receiver operating characteristic curves (AUCs) were compared using bootstrapping.Thirty percent (42/142) of lesions were non-evaluable on DWI; 23% (32/142) with image quality issues, 7% (10/142) with conspicuity and/or localization issues. Misregistration was the only factor associated with non-evaluability (P = 0.001). Smaller (≤10 mm) lesions were more commonly non-evaluable than larger lesions (p <0.03), though not significant after multiplicity correction. The AUC for differentiating benign and malignant lesions increased after excluding non-evaluable lesions, from 0.61 (95% CI: 0.50-0.71) to 0.75 (95% CI: 0.65-0.84).Image quality remains a technical challenge in breast DWI, particularly for smaller lesions. Protocol optimization and advanced acquisition and post-processing techniques would help to improve clinical utility.

Temperature dependent deuteron quadrupole echo line shapes are reported for integer generations, G = 1, 2, 3, 5, 7, 9, of polyamidoammonium chloride salts. The spectra are characteristic of amorphous materials undergoing broad glass transitions between 25 and 65 °C. Adequate fits of room-temperature line shapes were obtained by assuming Gaussian distributions of hydrogen bond lengths for interior R2ND···O and R3ND+···Cl- deuterons in the dendrimer spacers and Gaussian distributions of librational cone angles for motion of the C3v axes of terminal RND3+ groups. The estimated average hydrogen bond lengths at both types of interior site are 2.2 ± 0.15 Å, independent of generation number. For G = 2 the spectra obtained upon anion substitution of Cl- by Br- demonstrate that both types of hydrogen bonding are counterion dependent. In the temperature range from −30 to 60 °C the decreases in quadrupole coupling constants and increases in observed asymmetry parameters at the interior sites are ascribed to effects of planar libration. In the dendrimer interior the average amplitude of planar libration increases with temperature and decreases with increasing generation. Thermal energy is dissipated in counterion mobility for low generations and in mobility of the dendrimer spacers for high generations. In addition to 3-fold rotation, asymmetric cone libration is required to explain the observed temperature dependent asymmetry parameters of terminal ND3+ groups. For both low- and high-generation dendrimers the terminal groups buried in the interior exhibit smaller average librational amplitudes than those of generation 3.

The presented analysis of multisite, multiplatform clinical oncology trial data sought to enhance quantitative utility of the apparent diffusion coefficient (ADC) metric, derived from diffusion-weighted magnetic resonance imaging, by reducing technical interplatform variability owing to systematic gradient nonlinearity (GNL). This study tested the feasibility and effectiveness of a retrospective GNL correction (GNC) implementation for quantitative quality control phantom data, as well as in a representative subset of 60 subjects from the ACRIN 6698 breast cancer therapy response trial who were scanned on 6 different gradient systems. The GNL ADC correction based on a previously developed formalism was applied to trace-DWI using system-specific gradient-channel fields derived from vendor-provided spherical harmonic tables. For quantitative DWI phantom images acquired in typical breast imaging positions, the GNC improved interplatform accuracy from a median of 6% down to 0.5% and reproducibility of 11% down to 2.5%. Across studied trial subjects, GNC increased low ADC (&lt;1 µm2/ms) tumor volume by 16% and histogram percentiles by 5%–8%, uniformly shifting percentile-dependent ADC thresholds by ∼0.06 µm2/ms. This feasibility study lays the grounds for retrospective GNC implementation in multiplatform clinical imaging trials to improve accuracy and reproducibility of ADC metrics used for breast cancer treatment response prediction.

VERDICT (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) MRI is a multi b-value, variable diffusion time DWI sequence that allows generation of ADC maps from different b-value and diffusion time combinations. The aim was to assess precision of prostate ADC measurements from varying b-value combinations using VERDICT and determine which protocol provides the most repeatable ADC.Forty-one men (median age: 67.7 years) from a prior prospective VERDICT study (April 2016-October 2017) were analysed retrospectively. Men who were suspected of prostate cancer and scanned twice using VERDICT were included. ADC maps were formed using 5b-value combinations and the within-subject standard deviations (wSD) were calculated per ADC map. Three anatomical locations were analysed per subject: normal TZ (transition zone), normal PZ (peripheral zone), and index lesions. Repeated measures ANOVAs showed which b-value range had the lowest wSD, Spearman correlation and generalized linear model regression analysis determined whether wSD was related to ADC magnitude and ROI size.The mean lesion ADC for b0b1500 had the lowest wSD in most zones (0.18-0.58x10-4 mm2/s). The wSD was unaffected by ADC magnitude (Lesion: p = 0.064, TZ: p = 0.368, PZ: p = 0.072) and lesion Likert score (p = 0.95). wSD showed a decrease with ROI size pooled over zones (p = 0.019, adjusted regression coefficient = -1.6x10-3, larger ROIs for TZ versus PZ versus lesions). ADC maps formed with a maximum b-value of 500 s/mm2 had the largest wSDs (1.90-10.24x10-4 mm2/s).ADC maps generated from b0b1500 have better repeatability in normal TZ, normal PZ, and index lesions.

We are developing deep learning models for the segmentation of mouse tibia in MRI scans by utilizing three U-Net architectures: Attention, Inception, and basic U-Net, on a data set of 32 mice with 158 MRI scans. The data set was split into training (23 mice, 108 scans), validation (3 mice, 17 scans), and test (6 mice, 33 scans) sets. Two expert annotators (EA1 and EA2) provided manual 3D segmentations of the tibia on the MRI scans. EA1 provided outlines on all MRI scans, which were used as the reference for the training, validation, and testing of U-net models. EA2 provided outlines on the validation and test set, which were used for the assessment of inter-observer reference variability. The model performance was evaluated based on the average Jaccard index (%AJI), average volume intersection ratio (%AVI), average volume error (%AVE), and average Hausdorff distance (AHD, mm). For the test set, the %AJI with reference to EA1 was 83.45 &plusmn; 5.11 for the Attention U-Net, 83.05 &plusmn; 6.21 for the Inception U-Net, and 83.38 &plusmn; 5.36 for the basic U-Net. The %AJI was 80.70 &plusmn; 2.91 for EA1 versus EA2 and 79.70 &plusmn; 6.28 for Attention U-Net versus EA2. The variability between the U-Net models and EA1 and EA2 references was similar to the variability between EA1 and EA2. All 3 U-Net architectures achieved similar performances with the Attention U-Net performing marginally better.

Purpose To investigate the impact of longitudinal variation in functional tumor volume (FTV) underestimation and overestimation in predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Materials and Methods Women with breast cancer who were enrolled in the prospective I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) from May 2010 to November 2016 were eligible for this retrospective analysis. Participants underwent four MRI examinations during NAC treatment. FTV was calculated based on automated segmentation. Baseline FTV before treatment (FTV0) and the percentage of FTV change at early treatment and inter-regimen time points relative to baseline (∆FTV1 and ∆FTV2, respectively) were classified into high-standard or standard groups based on visual assessment of FTV under- and overestimation. Logistic regression models predicting pCR using single predictors (FTV0, ∆FTV1, and ∆FTV2) and multiple predictors (all three) were developed using bootstrap resampling with out-of-sample data evaluation with the area under the receiver operating characteristic curve (AUC) independently in each group. Results This study included 432 women (mean age, 49.0 years ± 10.6 [SD]). In the FTV0 model, the high-standard and standard groups showed similar AUCs (0.61 vs 0.62). The high-standard group had a higher estimated AUC compared with the standard group in the ∆FTV1 (0.74 vs 0.63), ∆FTV2 (0.79 vs 0.62), and multiple predictor models (0.85 vs 0.64), with a statistically significant difference for the latter two models (P = .03 and P = .01, respectively). Conclusion The findings in this study suggest that longitudinal variation in FTV estimation needs to be considered when using early FTV change as an MRI-based criterion for breast cancer treatment personalization. Keywords: Breast, Cancer, Dynamic Contrast-enhanced, MRI, Tumor Response ClinicalTrials.gov registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Ram in this issue.

This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.

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This paper reports on results of a multisite collaborative project launched by the MRI subgroup of Quantitative Imaging Network to assess current capability and provide future guidelines for generating a standard parametric diffusion map Digital Imaging and Communication in Medicine (DICOM) in clinical trials that utilize quantitative diffusion-weighted imaging (DWI). Participating sites used a multivendor DWI DICOM dataset of a single phantom to generate parametric maps (PMs) of the apparent diffusion coefficient (ADC) based on two models. The results were evaluated for numerical consistency among models and true phantom ADC values, as well as for consistency of metadata with attributes required by the DICOM standards. This analysis identified missing metadata descriptive of the sources for detected numerical discrepancies among ADC models. Instead of the DICOM PM object, all sites stored ADC maps as DICOM MR objects, generally lacking designated attributes and coded terms for quantitative DWI modeling. Source-image reference, model parameters, ADC units and scale, deemed important for numerical consistency, were either missing or stored using nonstandard conventions. Guided by the identified limitations, the DICOM PM standard has been amended to include coded terms for the relevant diffusion models. Open-source software has been developed to support conversion of site-specific formats into the standard representation.

To empirically corroborate vendor-provided gradient nonlinearity (GNL) characteristics and demonstrate efficient GNL bias correction for human brain apparent diffusion coefficient (ADC) across 3T MR systems and spatial locations.Spatial distortion vector fields (DVF) were mapped in 3D using a surface fiducial array phantom for individual gradient channels on three 3T MR platforms from different vendors. Measured DVF were converted into empirical 3D GNL tensors and compared with their theoretical counterparts derived from vendor-provided spherical harmonic (SPH) coefficients. To illustrate spatial impact of GNL on ADC, diffusion weighted imaging using three orthogonal gradient directions was performed on a volunteer brain positioned at isocenter (as a reference) and offset superiorly by 10-17 cm (>10% predicted GNL bias). The SPH tensor-based GNL correction was applied to individual DWI gradient directions, and derived ADC was compared with low-bias reference for human brain white matter (WM) ROIs.Empiric and predicted GNL errors were comparable for all three studied 3T MR systems, with <1.0% differences in the median and width of spatial histograms for individual GNL tensor elements. Median (±width) of ADC (10-3mm2/s) histograms measured at isocenter in WM reference ROIs from three MR systems were: 0.73 ± 0.11, 0.71 ± 0.14, 0.74 ± 0.17, and at off-isocenters (before versus after GNL correction) were respectively 0.63 ± 0.14 versus 0.72 ± 0.11, 0.53 ± 0.16 versus 0.74 ± 0.18, and 0.65 ± 0.16 versus 0.76 ± 0.18.The phantom-based spatial distortion measurements validated vendor-provided gradient fields, and accurate WM ADC was recovered regardless of spatial locations and clinical MR platforms using system-specific tensor-based GNL correction for routine DWI.

We have developed a peak deconvolution strategy that is applicable to the full mass range of a time-of-flight (TOF) spectrum. This strategy involves resampling a spectrum to create a time series that has equal peak widths (in time) across the entire spectrum, and then using the deconvolution filters we have previously described. We use this technique to deconvolve the protein mass spectra for blood serum and cell lysates acquired on three separate TOF instruments. Following deconvolution, we resolve spectral structures consistent with expected events such as multiply charged ions, matrix adducts and post-translational protein modifications. The deconvolution procedure produces a 40% improvement in the resolution and enhanced experimental sensitivity over the full length of the linear TOF record, up to m/z 150 000. This approach is particularly appropriate for automated data analysis and peak detection in dense TOF spectra.

Spatially nonuniform diffusion weighting bias as a result of gradient nonlinearity (GNL) causes substantial errors in apparent diffusion coefficient (ADC) maps for anatomical regions imaged distant from the magnet isocenter. Our previously described approach effectively removed spatial ADC bias from 3 orthogonal diffusion-weighted imaging (DWI) measurements for monoexponential media of arbitrary anisotropy. This work evaluates correction feasibility and performance for quantitative diffusion parameters of the 2-component intravoxel incoherent motion (IVIM) model for well-perfused and nearly isotropic renal tissue. Sagittal kidney DWI scans of a volunteer were performed on a clinical 3T magnetic resonance imaging scanner near isocenter and offset superiorly. Spatially nonuniform diffusion weighting caused by GNL resulted both in shifting and broadening of perfusion-suppressed ADC histograms for off-center DWI relative to unbiased measurements close to the isocenter. Direction-average diffusion weighting bias correctors were computed based on the known gradient design provided by the vendor. The computed bias maps were empirically confirmed by coronal DWI measurements for an isotropic gel-flood phantom. Both phantom and renal tissue ADC bias for off-center measurements was effectively removed by applying precomputed 3D correction maps. Comparable ADC accuracy was achieved for corrections of both b maps and DWI intensities in the presence of IVIM perfusion. No significant bias impact was observed for the IVIM perfusion fraction.

Journal of Magnetic Resonance ImagingVolume 49, Issue 7 p. i-i Cover ImageFree Access Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials Amita Shukla-Dave PhD, Corresponding Author Amita Shukla-Dave PhD davea@mskcc.org Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USAAddress reprint requests to: A.S.D., Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave., New York, NY 10065. E-mail: davea@mskcc.orgSearch for more papers by this authorNancy A. Obuchowski PhD, Nancy A. Obuchowski PhD Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio, USASearch for more papers by this authorThomas L. Chenevert PhD, Thomas L. Chenevert PhD Department of Radiology, University of Michigan, Ann Arbor, Michigan, USASearch for more papers by this authorSachin Jambawalikar PhD, Sachin Jambawalikar PhD Department of Radiology, Columbia University Irving Medical Center, New York, New York, USASearch for more papers by this authorLawrence H. Schwartz MD, Lawrence H. Schwartz MD Department of Radiology, Columbia University Irving Medical Center, New York, New York, USASearch for more papers by this authorDariya Malyarenko PhD, Dariya Malyarenko PhD Department of Radiology, University of Michigan, Ann Arbor, Michigan, USASearch for more papers by this authorWei Huang PhD, Wei Huang PhD Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USASearch for more papers by this authorSusan M. Noworolski PhD, Susan M. Noworolski PhD Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USASearch for more papers by this authorRobert J. Young MD, Robert J. Young MD Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USASearch for more papers by this authorMark S. Shiroishi MD, Mark S. Shiroishi MD Division of Neuroradiology, Department of Radiology, University of Southern California, Los Angeles, California, USASearch for more papers by this authorHarrison Kim PhD, MBA, Harrison Kim PhD, MBA Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama, USASearch for more papers by this authorCatherine Coolens PhD, Catherine Coolens PhD Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, CanadaSearch for more papers by this authorHendrik Laue PhD, Hendrik Laue PhD Department of Fraunhofer MEVIS, Bremen, GermanySearch for more papers by this authorCaroline Chung MD, Caroline Chung MD Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USASearch for more papers by this authorMark Rosen MD, PhD, Mark Rosen MD, PhD Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USASearch for more papers by this authorMichael Boss PhD, Michael Boss PhD Applied Physics Division, National Institute of Standards and Technology, Boulder, Colorado, USASearch for more papers by this authorEdward F. Jackson PhD, Edward F. Jackson PhD Departments of Medical Physics, Radiology, and Human Oncology, University of Wisconsin School of Medicine, Madison, Wisconsin, USASearch for more papers by this author Amita Shukla-Dave PhD, Corresponding Author Amita Shukla-Dave PhD davea@mskcc.org Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USAAddress reprint requests to: A.S.D., Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave., New York, NY 10065. E-mail: davea@mskcc.orgSearch for more papers by this authorNancy A. Obuchowski PhD, Nancy A. Obuchowski PhD Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio, USASearch for more papers by this authorThomas L. Chenevert PhD, Thomas L. Chenevert PhD Department of Radiology, University of Michigan, Ann Arbor, Michigan, USASearch for more papers by this authorSachin Jambawalikar PhD, Sachin Jambawalikar PhD Department of Radiology, Columbia University Irving Medical Center, New York, New York, USASearch for more papers by this authorLawrence H. Schwartz MD, Lawrence H. Schwartz MD Department of Radiology, Columbia University Irving Medical Center, New York, New York, USASearch for more papers by this authorDariya Malyarenko PhD, Dariya Malyarenko PhD Department of Radiology, University of Michigan, Ann Arbor, Michigan, USASearch for more papers by this authorWei Huang PhD, Wei Huang PhD Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USASearch for more papers by this authorSusan M. Noworolski PhD, Susan M. Noworolski PhD Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USASearch for more papers by this authorRobert J. Young MD, Robert J. Young MD Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USASearch for more papers by this authorMark S. Shiroishi MD, Mark S. Shiroishi MD Division of Neuroradiology, Department of Radiology, University of Southern California, Los Angeles, California, USASearch for more papers by this authorHarrison Kim PhD, MBA, Harrison Kim PhD, MBA Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama, USASearch for more papers by this authorCatherine Coolens PhD, Catherine Coolens PhD Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, CanadaSearch for more papers by this authorHendrik Laue PhD, Hendrik Laue PhD Department of Fraunhofer MEVIS, Bremen, GermanySearch for more papers by this authorCaroline Chung MD, Caroline Chung MD Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USASearch for more papers by this authorMark Rosen MD, PhD, Mark Rosen MD, PhD Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USASearch for more papers by this authorMichael Boss PhD, Michael Boss PhD Applied Physics Division, National Institute of Standards and Technology, Boulder, Colorado, USASearch for more papers by this authorEdward F. Jackson PhD, Edward F. Jackson PhD Departments of Medical Physics, Radiology, and Human Oncology, University of Wisconsin School of Medicine, Madison, Wisconsin, USASearch for more papers by this author First published: 05 June 2019 https://doi.org/10.1002/jmri.26805Citations: 3AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume49, Issue7Special Issue: Special Issue on the Value of MRIJune 2019Pages i-i RelatedInformation

The study aims to test the long-term stability of gradient characteristics for model-based correction of diffusion weighting (DW) bias in an apparent diffusion coefficient (ADC) for multisite imaging trials. Single spin echo (SSE) DWI of a long-tube ice-water phantom was acquired quarterly on six MR scanners over two years for individual diffusion gradient channels, along with B0 mapping, as a function of right-left (RL) and superior-inferior (SI) offsets from the isocenter. Additional double spin-echo (DSE) DWI was performed on two systems. The offset dependences of derived ADC were fit to 4th-order polynomials. Chronic shim gradients were measured from spatial derivatives of B0 maps along the tube direction. Gradient nonlinearity (GNL) was modeled using vendor-provided gradient field descriptions. Deviations were quantified by root-mean-square differences (RMSD), normalized to reference ice-water ADC, between the model and reference (RMSDREF), measurement and model (RMSDEXP), and temporal measurement variations (RMSDTMP). Average RMSDREF was 4.9 ± 3.2 (%RL) and –14.8 ± 3.8 (%SI), and threefold larger than RMSDEXP. RMSDTMP was close to measurement errors (~3%). GNL-induced bias across gradient systems varied up to 20%, while deviation from the model accounted at most for 6.5%, and temporal variation for less than 3% of ADC reproducibility error. Higher SSE RMSDEXP = 7.5–11% was reduced to 2.5–4.8% by DSE, consistent with the eddy current origin. Measured chronic shim gradients below 0.1 mT/m had a minor contribution to ADC bias. The demonstrated long-term stability of spatial ADC profiles and consistency with system GNL models justifies retrospective and prospective DW bias correction based on system gradient design models. Residual errors due to eddy currents and shim gradients should be corrected independent of GNL.

To assess for indirect evidence of gadoteridol retention in the deep brain nuclei of women undergoing serial screening breast MRI.This HIPAA-compliant prospective observational noninferiority imaging trial was approved by the IRB. From December 2016 to March 2018, 12 consented subjects previously exposed to 0-1 doses of gadoteridol (group 1) and 7 consented subjects previously exposed to ≥4 doses of gadoteridol (group 2) prospectively underwent research-specific unenhanced brain MRI including T1w spin echo imaging and T1 mapping. Inclusion criteria were: (1) planned breast MRI with gadoteridol, (2) no gadolinium exposure other than gadoteridol, (3) able to undergo MRI, (4) no neurological illness, (5) no metastatic disease, (6) no chemotherapy. Regions of interest were manually drawn in the globus pallidus, thalamus, dentate nucleus, and pons. Globus pallidus/thalamus and dentate nucleus/pons signal intensities and T1-time ratios were calculated using established methods and correlated with cumulative gadoteridol dose (mL).All subjects were female (mean age: 50 ± 12 years) and previously had received an average of 0.5 ± 0.5 (group 1) and 5.9 ± 2.1 (group 2) doses of gadoteridol (cumulative dose: 8 ± 8 and 82 ± 31 mL, respectively), with the last dose an average of 492 ± 299 days prior to scanning. There was no significant correlation between cumulative gadoteridol dose (mL) and deep brain nuclei signal intensity at T1w spin echo imaging (p = 0.365-0.512) or T1 mapping (p = 0.197-0.965).We observed no indirect evidence of gadolinium retention in the deep brain nuclei of women undergoing screening breast MRI with gadoteridol.

Clinical applications of quantitative diffusion-weighted imaging (qDWI) require confidence intervals for derived diffusion parameters to aid differentiation of technical errors from tissue characteristics. This study outlines practical procedures to evaluate precision (uncertainty) and accuracy (bias) of parametric maps derived for non-Gaussian diffusion models using numerical DWI phantoms (digital reference objects (DROs)) generated for advanced qDWI clinical trial protocols. The generated DROs include simulated acquisition noise, DICOM scaling and clinically-relevant qDWI parameter ranges for perfusion-fraction intra-voxel incoherent motion, kurtosis and stretched exponential diffusion models. Evaluation of fit accuracy and precision is illustrated for unsupervised linear least-squares (LLS) versus nonlinear minimization (NLM) algorithms ignorant of noise. DRO application examples are shown for calibration of noise-induced uncertainty with a physical DWI phantom, retrospective model fidelity analysis for brain tumor data and prospective error mapping in renal tissue. Physical DWI phantom analysis confirms adequate DRO noise model and realistic predicted fit errors. Detection of step-like bias activation in fit parameter space, consistent with modeldependent signal truncation at the noise floor, is proposed based on differences in NLM versus LLS fit maps. For all diffusion models, noise-induced bias in diffusivity is anti-correlated to bias in the model-specific (non-diffusivity) parameter. For confident assessment of fit parameter precision, bias minimization through b-range constraints and bdependent averaging is advised prior to fit uncertainty mapping. For low bias, higher precision is achieved by LLS versus NLM, and for diffusivity versus model parameter. The described procedures allow efficient qDWI protocol optimization toward reduced acquisition and model-dependent errors.

Abstract By applying time‐domain filters to time‐of‐flight (TOF) mass spectrometry signals, we have simultaneously smoothed and narrowed spectra resulting in improved resolution and increased signal‐to‐noise ratios. This filtering procedure has an advantage over detailed curve fitting of spectra in the case of large dense spectra, when neither the location nor the number of mass peaks is known a priori . This time series method is directly applicable in the time lag optimization range, where point density per peak is constant. We present a systematic methodology to optimize the filters according to any desired figure of merit, illustrating the procedure by optimizing the signal‐to‐noise per unit bandwidth of matrix‐assisted laser desorption/ionization (MALDI) data. We also introduce a nonlinear filter that reduces the spurious structure that often accompanies deconvolution filters. The net result of the application of these filters is that we can identify new structures in dense MALDI‐TOF data, clearly showing small adducts to heavy biomolecules. Copyright © 2006 John Wiley &amp; Sons, Ltd.

This chapter provides a motivation for standardization, quality control, and site qualification, as well as their applications to quantitative imaging biomarkers. Motivating this effort is a comment made in 2011 by George Poste: “A major impediment to progress in the hunt for biomarkers is the lack of standardization in how specimens are collected.” The term “biomarkers,” in medicine, applies equally well to qualitative and quantitative image biomarkers and to the information that can be extracted from them. Standards, phantoms, and site qualifications can be either specific for a defined medical imaging task, or general purpose, to increase confidence across a broad range of medical tasks, or a mix of task-specific and general purpose. In all cases, there are trade-offs in cost, complexity, and utility.

Nonexponential recovery curves have been obtained for anisotropic deuteron spin lattice relaxation of deuterated polyamidoammonium chloride salts for integer generations G = 1, 2, 3, 5, 7, and 9, at room temperature. The temperature dependence of the relaxation of generations 2, 3, and 9 is reported. Multiple exponential relaxation behavior is ascribed to a sum of single exponential, anisotropic contributions from overlapping quadrupole echo powder patterns. These are assigned to interior secondary amide, R2ND, deuterated tertiary amine, R3ND+, and terminal ammonium, RND3+, deuterons in the glassy polymer. Additional temperature dependent features in the middle of the spectra arise from mobile phases. For each type of deuteron the best fit of the relaxation data is achieved by numerically solving the stochastic Liouville equation for the relevant motional model. Planar libration of R2ND and R3ND+ deuterons is simulated by jumps along an arc. Motion of RND3+ groups is modeled as a two-frame process in which ND3+ rotation proceeds at by jumps about the pseudo 3-fold R−N bond axis, which in turn jumps at among sites in a cone. Low, intermediate and high generation subclasses of solid dendrimers are defined on the basis of rates and activation energies for libration and rotation of their spacer groups, branch points, and termini. There are two superimposed powder patterns for terminal ammonium deuterons, distinguished only by their different relaxation behavior. These arise from fast (1010 s-1) and slow (109 s-1) ammonium rotors. For low and high generations, the fast rotors represent a small fraction (<30% at ambient temperature) of less constrained ammonium groups. With increasing temperature, the fraction of fast rotors decreases for G2 and G9, but increases for G3. Data reported here quantify a model in which dendrimer arms of G3 are neither extensively interpenetrated (as for G2) nor backfolded (as for G9).

Abstract A computational technique is presented for the automated assignment of the multiple charge and multimer states (ionization states) in the time‐of‐flight (TOF) domain for matrix‐assisted laser desorption/ionization (MALDI) spectra. Examples of the application of this technique include an improved, automatic calibration over the 2 to 70 kDa mass range and a reduced data redundancy after reconstruction of the molecular spectrum of only singly charged monomers. This method builds on our previously reported enhancement of broad‐mass signal detection, and includes two steps: (1) an automated correction of the instrumental acquisition initial time delay, and (2) a recursive TOF detection of multiple charge states and singly charged multimers of molecular [MH] + ions over the entire record range, based on MALDI methods. The technique is tested using calibration mixtures and pooled serum quality control samples acquired along with clinical study data. The described automated procedure improves the analysis and dimension reduction of MS data for comparative proteomics applications. Copyright © 2009 John Wiley &amp; Sons, Ltd.

Abstract Purpose: To demonstrate robust detection of biomarkers in broad‐mass‐range TOF‐MS data. Experimental Design: Spectra were obtained for two serum protein profiling studies: (i) 2–200 kDa for 132 patients, 67 healthy and 65 diagnosed as having adult T‐cell leukemia and (ii) 2–100 kDa for 140 patients, 70 pairs, each with matched prostate‐specific antigen (PSA) levels and biopsy‐confirmed diagnoses of one benign and one prostate cancer. Signal processing was performed on raw spectra and peak data were normalized using four methods. Feature selection was performed using Bayesian Network Analysis and a classifier was tested on withheld data. Identification of candidate biomarkers was pursued. Results: Integrated peak intensities were resolved over full spectra. Normalization using local noise values was superior to global methods in reducing peak correlations, reducing replicate variability and improving feature selection stability. For the leukemia data set, potential disease biomarkers were detected and were found to be predictive for withheld data. Preliminary assignments of protein IDs were consistent with published results and LC‐MS/MS identification. No prostate‐specific‐antigen‐independent biomarkers were detected in the prostate cancer data set. Conclusions and clinical relevance: Signal processing, local signal‐to‐noise (SNR) normalization and Bayesian Network Analysis feature selection facilitate robust detection and identification of biomarker proteins in broad‐mass‐range clinical TOF‐MS data.

To demonstrate a method for quantification of impeded diffusion fraction (IDF) using conventional clinical DWI protocols.The IDF formalism is introduced to quantify contribution from water coordinated by macromolecules to DWI voxel signal based on fundamentally different diffusion constants in vascular capillary, bulk free, and coordinated water compartments. IDF accuracy was studied as a function of b-value set. The IDF scaling with restricted compartment size and polyvinylpirrolidone (PVP) macromolecule concentration was compared to conventional apparent diffusion coefficient (ADC) and isotropic kurtosis model parameters for a diffusion phantom. An in vivo application was demonstrated for six prostate cancer (PCa) cases with low and high grade lesions annotated from whole mount histopathology.IDF linearly scaled with known restricted (vesicular) compartment size and PVP concentration in phantoms and increased with histopathologic score in PCa (from median 9% for atrophy up to 60% for Gleason 7). IDF via non-linear fit was independent of b-value subset selected between b = 0.1 and 2 ms/µm2 , including standard-of-care (SOC) PCa protocol. With maximum sensitivity for high grade PCa, the IDF threshold below 51% reduced false positive rate (FPR = 0/6) for low-grade PCa compared to apparent diffusion coefficient (ADC > 0.81 µm2 /ms) of PIRADS PCa scoring (FPR = 3/6).The proposed method may provide quantitative imaging assays of cancer grading using common SOC DWI protocols.

To more fully realize the promise of advanced breast diffusion-weighted imaging (DWI) methods in the clinical environment, repeatability and reproducibility of quantitative diffusion-based imaging results need to be assessed and maximized across vendor platforms and clinical sites. Identification of sources of variability and their possible remediation should precede initiation of costly multicenter trials. In particular, gains in consistency are realized by standardization of DWI scan sequences performed across diverse hardware platforms. Standardization in image acquisition protocol includes not only specification of obvious top-level parameters such as b value set, gradient timing, and scan geometry but also secondary-level settings that impact image quality, such as those related to fat suppression, readout bandwidth, phase-encode echo-spacing, and acceleration options. In practice, vendor-specific terminology and default DWI sequence design vary with platform and present a significant challenge to standardization. Reasonably “harmonized” DWI acquisition protocols should be defined in parallel for each clinical scanner platform. Consideration for standardized image processing pipeline and analysis methods are also important. Use of a “central core lab” for image collection, potential vendor-specific DWI sorting and quantitative diffusion metric map generation, and consistency in downstream analysis support overall standardization. This chapter will address breast DWI performance evaluation for clinical applications, overview test objects for quality control, provide guidance on how to standardize DWI protocols, and control conditions toward accurate and reproducible results.

Recently developed quantitative phantom based on lamellar-vesicles provides the range of tissue relevant diffusion kurtosis parameters for accurate evaluation of advanced multi-b DWI protocols and parametric diffusion models. This work studies temperature dependence of phantom diffusion kurtosis parameters to supply accurate nominal parameter values for typical scan room temperature range.

Apparent diffusion coefficient (ADC) metric is evaluated as a potential alternative to biopsy for disease grading and therapy response assessment in bone marrow of myelofibrosis (MF) patients. Spatially dependent bias in diffusion weighting due to systematic gradient nonlinearity (GNL) results in false heterogeneity of ADC maps over the imaged bone space. Here we illustrate deployment of prospective GNL bias correction based on technology developed in an academic industrial partnership to reduce technical variability of ADC in a MF clinical imaging trial.

Nanoparticle lipid vesicles are developed with controllable water exchange to provide a ground-truth benchmark for MR methods which measure exchange. Diffusion data are collected as a function of diffusion delay &amp;Delta;. An exchange time, &amp;tau;, is measured to be 164 ms in a phantom with a highly fluid membrane. These materials should help to clarify water dynamics in complex systems.

Purpose Biopsy remains the clinical standard for evaluating bone marrow of patients with myelofibrosis (MF). However, randomly sampling a small volume of bone marrow, typically from the ilium, fails to capture heterogeneity of disease across anatomic sites. The invasive, painful nature of the procedure limits patient acceptance of repeated biopsies to monitor disease status and response to therapy. Here, we present a non-invasive, MRI technique for evaluating bone marrow in MF. Participants and Methods We analyzed bone marrow of 66 study participants (45 with MF; 15 with essential thrombocytosis or polycythemia vera; and 6 healthy controls). We assessed participant bone marrow across three anatomic sites (vertebral bodies, ilium, and femoral heads), using three quantitative MRI sequences: proton density fat fraction (PDFF) for fat content; apparent diffusion coefficient (ADC) for cellular effect on water mobility; and magnetization transfer (MT) for macromolecular structure and fiber deposition. We correlated these MRI metrics with patient prognosis (dynamic international prognostic scoring system; DIPSS) and iliac biopsy data for cellularity and fibrosis (MF grade). Results Participants with MF had elevated MT ratio (MTR) and ADC and lower PDFF compared to healthy participants. Non-MF MPN participants demonstrated the same trend, though to a lesser extent. Individual MRI metrics correlated strongly across anatomic sites (r pearson from 0.57 to 0.89, p-value &amp;lt; 0.01), with notable heterogeneity both within and among different regions. DIPSS risk groups had limited correlation with MRI metrics. To understand to what extent quantitative MRI biomarkers identify fibrotic marrow, we developed a multivariate logistic regression model to stratify the 45 participants with MF by early fibrosis (MF grades 0-1) and overt fibrosis (MF grades 2-3). Of the seven total anatomy-MRI metric combinations, three had the greatest contributions toward grading marrow fibrosis: PDFF in the vertebral bodies and ilium along with ADC in the ilium. We developed the logistic regression model to predict early and overt fibrosis with these three parameters using a 70%/30% train/test split with 5-fold cross validation. Based on this model, quantitative MRI metrics are associated with the severity of fibrosis in the bone marrow (test set performance: accuracy = 84.6%, sensitivity to predict overt fibrosis = 88.9%, specificity = 75%, area under the receiver operator curve = 0.94). Conclusion Quantitative bone marrow MRI reliably captures relevant metrics of MF bone marrow, including cellularity and fat replacement by fibrosis, and may be useful in clinical drug trials and patient management for MF.

Protocol standardization and optimization for clinical translation of emerging quantitative multiparametric (mp)MRI biomarkers of high-risk prostate cancer requires imaging references that mimic realistic tissue value combinations for bias assessment in derived relaxation and diffusion parameters.This work aimed to develop a novel class of hydrogel-based synthetic materials with simultaneously controlled quantitative relaxation, diffusion, and kurtosis parameters that mimic in vivo prostate value combinations in the same spatial compartment and allow stable assemblies of adjacent structures.A set of materials with tunable T2 , diffusion, and kurtosis were assembled to create quantitative biomimetic (mp)MRI references. T2 was controlled with variable agarose concentration, monoexponential diffusion by polyvinylpyrrolidone (PVP), and kurtosis by addition of lamellar vesicles. The materials were mechanically stabilized by UV cross-linked polyacrylamide gels (PAG) to allow biomimetic morphologies. The reference T2 were measured on a 3T scanner using multi-echo CPMG, and diffusion kurtosis-with multi-b DWI.Agarose concentration controls T2 values which are nominally independent of PVP or vesicle concentration. For agarose PVP hydrogels, monoexponential diffusion values are a function of PVP concentration and independent of agarose concentration. Compared to free vesicles, for agarose-PAG combined with vesicles, diffusion was predominantly controlled by vesicles and PAG, while kurtosis was affected by agarose and vesicle concentration. Both hydrogel classes achieved image voxel parameter values (T2 , Da , Ka ) for relaxation (T2 : 65-255 ms), apparent diffusion (Da : 0.8-1.7 μm2 /ms), and kurtosis (Ka : 0.5-1.25) within the target literature ranges for normal prostate zones and cancer lesions. Relaxation and diffusion parameters remained stable for over 6 months for layered material assemblies.A stable biomimetic mpMR reference based on hydrogels has been developed with a range of multi-compartment diffusion and relaxation parameter combinations observed in cancerous and healthy prostate tissue.

ADVERTISEMENT RETURN TO ISSUEPREVNoteQuantitative Characterization of Librational Rate Distributions for G2 PAMAM Dendrimer Salt from Deuteron Magic Angle Spinning SpectraDariya I. Malyarenko, Robert L. Vold, and Gina L. HoatsonView Author Information Departments of Applied Science and Physics, The College of William and Mary, P.O. Box 8795, Williamsburg, Virginia 23187-8795 Cite this: Macromolecules 2001, 34, 22, 7911–7915Publication Date (Web):September 27, 2001Publication History Received29 March 2001Published online27 September 2001Published inissue 1 October 2001https://doi.org/10.1021/ma0105385Copyright © 2001 American Chemical SocietyRequest reuse permissions Article Views69Altmetric-Citations4LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (90 KB) Get e-AlertscloseSUBJECTS:Activation energy,Cations,Dendrons,Glass transition,Granular materials Get e-Alerts

The use of mass spectrometry to obtain molecular profiles indicative of alteration of concentrations of peptides in body fluids is currently the subject of intense investigation. For surface-based time-of-flight mass spectrometry the reliability and specificity of such profiling methods depend both on the resolution of the measuring instrument and on the preparation of samples. The present work is a part of a program to use Ga+ beam TOF-SIMS alone, and as an adjunct to MALDI, in the development of reliable protein and peptide markers for diseases. Here, we describe techniques to prepare samples of relatively high-mass peptides, which serve as calibration standards and proxies for biomarkers. These are: Arg8-vasopressin, human angiotensin II, and somatostatin. Their TOF-SIMS spectra show repeatable characteristic features, with mass resolution exceeding 2000, including parent peaks and chemical adducts. The lineshape analysis for high-resolution parent peaks is shown to be useful for filter construction and deconvolution of inferior resolution SELDI-TOF spectra of calibration peptide mixture.

PURPOSE: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers. METHODS: A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create “variable signal,” whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity. RESULTS: Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images. CONCLUSION: Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.

Myeloproliferative neoplasms (MPNs), a class of blood cancers, typically arise from mutations in the thrombopoietin receptor (MPL), JAK2, or calreticulin that constitutively activate JAK/STAT signaling in hematopoietic stem and progenitor cells. Myelofibrosis (MF), the most aggressive of the MPNs, can arise as a primary neoplasm or secondary to other MPNs. MF is characterized by progressive fibrosis of bone marrow (BM), destruction of the hematopoietic niche, and eventual BM failure. Recently, the focus of treatment in MF has shifted toward restoration of healthy BM architecture and a functional hematopoietic niche rather than focusing solely on alleviating symptoms. Accomplishing this paradigm shift in drug development and therapy requires new approaches to monitor spatial and temporal changes in BM in patients during treatment. We present a quantitative magnetic resonance imaging (MRI) approach to analyze key pathologic changes in BM in a mouse model of MPN driven by MPLW515L. To evaluate disease extent, progression, and response to therapy in mice, we use anatomical MRI for spleen volume and three quantitative MRI metrics for BM: 1) apparent diffusion coefficient (ADC), 2) proton density fat fraction (PDFF), and 3) magnetization transfer ratio (MTR). PDFF and ADC assess the fat content and water movement of the BM and are useful in identifying cellularity changes such as hypercellularity in MPNs and MF. MTR probes changes in macromolecular structure, such as those observed with increasing fibrosis in MF. We used quantitative MRI to measure response to treatment in BM and change in spleen volume in response to three drugs: ruxolitinib (JAK1/2 inhibitor), fedratinib (JAK2 inhibitor), and navitoclax (BCL-2/BCL-xL inhibitor). Within 10 days after starting treatment, diseased spleen volumes in the ruxolitinib group (~3.5x healthy volume) had the greatest average reduction in spleen volume measured by MRI (-73.5% ± 11.4%) followed by the fedratinib group (-52.7% ± 16.7%). Interestingly, the spleens of every ruxolitinib-treated mouse displayed consistent and prolonged spleen volume reductions, while only a subset of mice in the other treatment groups (~70% fedratinib mice; ~33% navitoclax mice) demonstrated reductions in spleen volume. Despite the robust spleen response in ruxolitinib-treated mice, only some of the mice (~60%) exhibited sustained changes in BM ADC, MTR, and PDFF signals expected with reversion to healthy BM (decreased ADC, decreased MTR, and increased PDFF). In mice treated with fedratinib or navitoclax, even fewer mice (~40% and ~33% respectively) showed reversion toward healthy BM. Importantly, we observed spatial heterogeneity of BM response within the tibia of single mice, where proximal and distal tibia regions often responded disparately (~49% of cases). Because improving BM cellularity and fibrosis is increasingly the target of therapy in MPNs and in MF specifically, there is a distinct need for better techniques for longitudinally evaluating variations in treatment efficacy in living subjects. In this study, we demonstrate that the combination of three quantitative MRI metrics (ADC, PDFF, and MTR) can detect the impact of therapy on the BM in a preclinical mouse model of MPN/MF. Interestingly, we identified pronounced heterogeneity at multiple levels during treatment: 1) differences in changes in spleen volume versus BM; 2) disparate treatment responses of mice within a treatment group; and 3) contrasting regional treatment responses of the BM within a single mouse. Because these quantitative MRI metrics are directly translatable to clinical medicine and can identify variations in treatment efficacy in BM, both between and within subjects, this study sets the stage for future drug development and analysis of therapeutic interventions on the BM of patients with MF.