Jing Yuan

The development of a city gradually fosters different functional regions, such as educational areas and business districts. In this paper, we propose a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among regions and points of interests (POIs) located in a region. Specifically, we segment a city into disjointed regions according to major roads, such as highways and urban express ways. We infer the functions of each region using a topic-based inference model, which regards a region as a document, a function as a topic, categories of POIs (e.g., restaurants and shopping malls) as metadata (like authors, affiliations, and key words), and human mobility patterns (when people reach/leave a region and where people come from and leave for) as words. As a result, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. We further identify the intensity of each function in different locations. The results generated by our framework can benefit a variety of applications, including urban planning, location choosing for a business, and social recommendations. We evaluated our method using large-scale and real-world datasets, consisting of two POI datasets of Beijing (in 2010 and 2011) and two 3-month GPS trajectory datasets (representing human mobility) generated by over 12,000 taxicabs in Beijing in 2010 and 2011 respectively. The results justify the advantages of our approach over baseline methods solely using POIs or human mobility.

GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest route. We build our system based on a real-world trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70% of the routes suggested by our method are faster than the competing methods, and 20% of the routes share the same results. On average, 50% of our routes are at least 20% faster than the competing approaches.

This paper presents a Cloud-based system computing customized and practically fast driving routes for an end user using (historical and real-time) traffic conditions and driver behavior. In this system, GPS-equipped taxicabs are employed as mobile sensors constantly probing the traffic rhythm of a city and taxi drivers' intelligence in choosing driving directions in the physical world. Meanwhile, a Cloud aggregates and mines the information from these taxis and other sources from the Internet, like Web maps and weather forecast. The Cloud builds a model incorporating day of the week, time of day, weather conditions, and individual driving strategies (both of the taxi drivers and of the end user for whom the route is being computed). Using this model, our system predicts the traffic conditions of a future time (when the computed route is actually driven) and performs a self-adaptive driving direction service for a particular user. This service gradually learns a user's driving behavior from the user's GPS logs and customizes the fastest route for the user with the help of the Cloud. We evaluate our service using a real-world dataset generated by over 33,000 taxis over a period of 3 months in Beijing. As a result, our service accurately estimates the travel time of a route for a user; hence finding the fastest route customized for the user.

Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p<0.05) and had an efficient implementation with a run time of 8 min and 3 s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/.

Urban computing for city planning is one of the most significant applications in Ubiquitous computing. In this paper we detect flawed urban planning using the GPS trajectories of taxicabs traveling in urban areas. The detected results consist of 1) pairs of regions with salient traffic problems and 2) the linking structure as well as correlation among them. These results can evaluate the effectiveness of the carried out planning, such as a newly built road and subway lines in a city, and remind city planners of a problem that has not been recognized when they conceive future plans. We conduct our method using the trajectories generated by 30,000 taxis from March to May in 2009 and 2010 in Beijing, and evaluate our results with the real urban planning of Beijing.

Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet that connects each layer to every other layer in a feed-forward fashion and has shown impressive performances in natural image classification tasks. We propose HyperDenseNet, a 3-D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path but also between those across different paths. This contrasts with the existing multi-modal CNN approaches, in which modeling several modalities relies entirely on a single joint layer (or level of abstraction) for fusion, typically either at the input or at the output of the network. Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation. We report extensive evaluations over two different and highly competitive multi-modal brain tissue segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing on six month infant data and the latter on adult images. HyperDenseNet yielded significant improvements over many state-of-the-art segmentation networks, ranking at the top on both benchmarks. We further provide a comprehensive experimental analysis of features re-use, which confirms the importance of hyper-dense connections in multi-modal representation learning. Our code is publicly available.

This paper presents a smart driving direction system leveraging the intelligence of experienced drivers. In this system, GPS-equipped taxis are employed as mobile sensors probing the traffic rhythm of a city and taxi drivers' intelligence in choosing driving directions in the physical world. We propose a time-dependent landmark graph to model the dynamic traffic pattern as well as the intelligence of experienced drivers so as to provide a user with the practically fastest route to a given destination at a given departure time. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest and customized route for end users. We build our system based on a real-world trajectory data set generated by over 33,000 taxis in a period of three months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70 percent of the routes suggested by our method are faster than the competing methods, and 20 percent of the routes share the same results. On average, 50 percent of our routes are at least 20 percent faster than the competing approaches.

We present a recommender for taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers' mobility patterns and 2) taxi drivers' pick-up behaviors learned from the GPS trajectories of taxicabs. First, this recommender provides taxi drivers with some locations and the routes to these locations, towards which they are more likely to pick up passengers quickly (during the routes or at these locations) and maximize the profit. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we learn the above knowledge (represented by probabilities) from GPS trajectories of taxis. Then, we feed the knowledge into a probabilistic model which estimates the profit of the candidate locations for a particular driver based on where and when the driver requests for the recommendation. We validate our recommender using historical trajectories generated by over 12,000 taxis during 110 days.

The detection of outliers in spatio-temporal traffic data is an important research problem in the data mining and knowledge discovery community. However to the best of our knowledge, the discovery of relationships, especially causal interactions, among detected traffic outliers has not been investigated before. In this paper we propose algorithms which construct outlier causality trees based on temporal and spatial properties of detected outliers. Frequent substructures of these causality trees reveal not only recurring interactions among spatio-temporal outliers, but potential flaws in the design of existing traffic networks. The effectiveness and strength of our algorithms are validated by experiments on a very large volume of real taxi trajectories in an urban road network.

Matching a raw GPS trajectory to roads on a digital map is often referred to as the Map Matching problem. However, the occurrence of the low-sampling-rate trajectories (e.g. one point per 2 minutes) has brought lots of challenges to existing map matching algorithms. To address this problem, we propose an Interactive Voting-based Map Matching (IVMM) algorithm based on the following three insights: 1) The position context of a GPS point as well as the topological information of road networks, 2) the mutual influence between GPS points (i.e., the matching result of a point references the positions of its neighbors; in turn, when matching its neighbors, the position of this point will also be referenced), and 3) the strength of the mutual influence weighted by the distance between GPS points (i.e., the farther distance is the weaker influence exists). In this approach, we do not only consider the spatial and temporal information of a GPS trajectory but also devise a voting-based strategy to model the weighted mutual influences between GPS points. We evaluate our IVMM algorithm based on a user labeled real trajectory dataset. As a result, the IVMM algorithm outperforms the related method (ST-Matching algorithm).

Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/).

To determine the diagnostic performance of diffusion-weighted (DW) imaging for the prediction of treatment failure in primary head and neck squamous cell carcinoma (HNSCC).The study was approved by the local institutional ethics committee and conducted with informed written consent in patients with primary HNSCC treated with radiation therapy and chemotherapy. DW imaging of the primary tumor was performed before treatment in 37 patients and was repeated within 2 weeks of treatment in 30 patients. Histograms of apparent diffusion coefficients (ADCs) were analyzed, and mean ADC, kurtosis, skewness, and their respective percentage change were correlated for local failure and local control at 2 years by using the Student t test. Univariate and multivariate analyses of the ADC parameters, T stage, and tumor volume were performed by using logistic regression for prediction of local failure.Local failure occurred in 16 of 37 (43%) patients and local control occurred in 21 of 37 (57%) patients. Pretreatment ADC parameters showed no correlation with local failure. There was significant intratreatment increase in mean ADC and a decrease in skewness and kurtosis (P < .001, P < .001, P = .024, respectively) for the whole group of patients when compared with those before treatment. During treatment, primary tumors showed a significantly lower increase in percentage change of mean ADC, higher skewness, and higher kurtosis for local failure than for local control (P = .016, .015, and .040, respectively). These ADC parameters also were significant for predicting local failure with use of univariate but not multivariate analysis.Early intratreatment DW imaging has the potential to allow prediction of treatment response at the primary site in patients with HNSCC.

We propose and investigate novel max-flow models in the spatially continuous setting, with or without supervised constraints, under a comparative study of graph based max-flow / min-cut.We show that the continuous max-flow models correspond to their respective continuous min-cut models as primal and dual problems, and the continuous min-cut formulation without supervision constraints regards the well-known Chan-Esedoglu-Nikolova model [15] as a special case.In this respect, basic conceptions and terminologies applied by discrete max-flow / mincut are revisited under a new variational perspective.We prove that the associated nonconvex partitioning problems, unsupervised or supervised, can be solved globally and exactly via the proposed convex continuous max-flow and min-cut models.Moreover, we derive novel fast max-flow based algorithms whose convergence can be guaranteed by standard optimization theories.Experiments on image segmentation, both unsupervised and supervised, show that our continuous max-flow based algorithms outperform previous approaches in terms of efficiency and accuracy.

This paper is devoted to the optimization problem of continuous multi-partitioning, or multi-labeling, which is based on a convex relaxation of the continuous Potts model. In contrast to previous efforts, which are tackling the optimal labeling problem in a direct manner, we first propose a novel dual model and then build up a corresponding duality-based approach. By analyzing the dual formulation, sufficient conditions are derived which show that the relaxation is often exact, i.e. there exists optimal solutions that are also globally optimal to the original nonconvex Potts model. In order to deal with the nonsmooth dual problem, we develop a smoothing method based on the log-sum exponential function and indicate that such a smoothing approach leads to a novel smoothed primal-dual model and suggests labelings with maximum entropy. Such a smoothing method for the dual model also yields a new thresholding scheme to obtain approximate solutions. An expectation maximization like algorithm is proposed based on the smoothed formulation which is shown to be superior in efficiency compared to earlier approaches from continuous optimization. Numerical experiments also show that our method outperforms several competitive approaches in various aspects, such as lower energies and better visual quality.

The advance of object tracking technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data stream. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from trajectory stream. Such technique has broad applications in the areas of scientific study, transportation management and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the algorithm's efficiency. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A new data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery on trajectory stream. The traveling buddies are micro-groups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. The buddy-based method is an order of magnitude faster than existing methods. It also outperforms other competitors with higher precision and recall in companion discovery.

To correlate spin-lattice relaxation time in the rotating frame (T1ρ) measurements with degree of liver fibrosis in a rat model.The protocols and procedures were approved by the local Animal Experimentation Ethics Committee. Liver fibrosis was induced with biliary duct ligation (BDL). Two studies, 1 month apart, were performed with a 3-T clinical imager. The first study involved longitudinal magnetic resonance (MR) imaging follow-up of BDL rats (n = 8) and control rats (n = 4) on days 8, 15, 21, and 29 after BDL. The second study involved MR imaging of another group of BDL and control rats (n = 5 for each) on days 24 and 38 after BDL. Hematoxylin-eosin and picrosirius red staining were performed in liver specimens from days 8, 15, 24, and 38 after BDL. Repeated-measures analysis of variance was used, and treatment groups were compared (Bonferroni adjustment).On day 8, there were proliferation of bile duct and inflammatory cell infiltration around portal triads. While there was overlap, BDL rats (n = 8) demonstrated higher mean liver T1ρ values than did control rats (n = 4) on day 8 (46.7 msec ± 2.9 [standard deviation] vs 44.7 msec ± 1.2, P = .4). On day 15, BDL rats demonstrated liver fibrosis with a background of inflammatory infiltration. On day 15, mean T1ρ values in BDL rats could be largely separated from those in control rats (52.6 msec ± 6.0 vs 43.8 msec ± 1.5, P = .02). On day 24, BDL rats had liver T1ρ values 23.5% higher than in control rats (n = 5 for each group, P = .0007). Histomorphometric analysis showed that collagen content increased after surgery from days 8 to 24 (n = 6 for each group, P < .0001), with no further increase between days 24 and 38 (n = 6 for each group, P >.99).In this model, liver fibrosis was detected with T1ρ MR imaging; the degree of fibrosis was correlated with degree of increase in T1ρ measurements.http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101638/-/DC1.

A comprehensive literature review was performed on liver intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) technique and its applications. Heterogeneous data have been reported. IVIM parameters are magnetic field strength dependent to a mild extent. A lower Dslow (D) value at 3 T than at 1.5 T and higher perfusion fraction (PF) value at 3 T than at 1.5 T were noted. An increased number of b values are associated with increased IVIM parameter measurement accuracy. With the current status of art, IVIM technique is not yet capable of detecting early stage liver fibrosis and diagnosing liver fibrosis grades, nor can it differentiate liver tumors. Though IVIM parameters show promise for tumor treatment monitoring, till now how PF and Dfast (D*) add diagnostic value to Dslow or apparent diffusion coefficient (ADC) remains unclear. This paper shows the state-of-art IVIM MR technique is still not able to offer reliable measurement for liver. More works on the measurement robustness are warranted as they are essential to justify follow-up clinical studies on patients.

We address the continuous problem of assigning multiple (unordered) labels with the minimum perimeter. The corresponding discrete Potts model is typically addressed with a-expansion which can generate metrication artifacts. Existing convex continuous formulations of the Potts model use TV-based functionals directly encoding perimeter costs. Such formulations are analogous to 'min-cut' problems on graphs. We propose a novel convex formulation with a continous 'max-flow' functional. This approach is dual to the standard TV-based formulations of the Potts model. Our continous max-flow approach has significant numerical advantages; it avoids extra computational load in enforcing the simplex constraints and naturally allows parallel computations over different labels. Numerical experiments show competitive performance in terms of quality and significantly reduced number of iterations compared to the previous state of the art convex methods for the continuous Potts model.

Purpose To investigate the saturation‐power dependence of amide proton transfer (APT)‐weighted and nuclear Overhauser enhancement‐weighted image contrasts in a rat glioma model at 4.7 T. Methods The 9L tumor‐bearing rats ( n = 8) and fresh chicken eggs ( n = 4) were scanned on a 4.7‐T animal magnetic resonance imaging scanner. Z‐spectra over an offset range of ±6 ppm were acquired with different saturation powers, followed by the magnetization transfer‐ratio asymmetry analyses around the water resonance. Results The nuclear Overhauser enhancement signal upfield from the water resonance (−2.5 to −5 ppm) was clearly visible at lower saturation powers (e.g., 0.6 µT) and was larger in the contralateral normal brain tissue than in the tumor. Conversely, the APT effect downfield from the water resonance was maximized at relatively higher saturation powers (e.g., 2.1 µT) and was larger in the tumor than in the contralateral normal brain tissue. The nuclear Overhauser enhancement decreased the APT‐weighted image signal, based on the magnetization transfer‐ratio asymmetry analysis, but increased the APT‐weighted image contrast between the tumor and contralateral normal brain tissue. Conclusion The APT and nuclear Overhauser enhancement image signals in tumor are maximized at different saturation powers. The saturation power of roughly 2 μT is ideal for APT‐weighted imaging at clinical B 0 field strengths. Magn Reson Med 70:320–327, 2013. © 2013 Wiley Periodicals, Inc.

Although increasing evidence suggests a role for the gut microbiota in neurodevelopment, the actual structure and composition of microbiota in children with attention-deficit/hyperactivity disorder (ADHD) remain unclear. Thus, the present study aimed to define the characteristics of gut microbiota in treatment-naive children with ADHD and to assess their relationship with the severity of ADHD symptoms. High-throughput pyrosequencing was used to investigate the microbiota composition in fecal matter from 51 children with ADHD and 32 healthy controls (HC). An operational taxonomical unit (OTU)-level analysis revealed a significant decrease in the fractional representation of Faecalibacterium in children with ADHD compared to HC. In individuals with ADHD, the abundance of Faecalibacterium was negatively associated with parental reports of ADHD symptoms. However, there was no significant difference in alpha diversity between the ADHD and control groups. This present findings support the involvement of microbiota alteration in psychiatric diseases and Faecalibacterium may represent a potential novel marker of gut microbiota in ADHD. Future studies are needed to validate these findings and to elucidate the temporal and causal relationships between these variables.

Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain development. However, computing such segmentations is very challenging, especially for 6-month infant brain, due to the poor image quality, among other difficulties inherent to infant brain MRI, e.g., the isointense contrast between white and gray matter and the severe partial volume effect due to small brain sizes. This study investigates the problem with an ensemble of semi-dense fully convolutional neural networks (CNNs), which employs T1-weighted and T2-weighted MR images as input. We demonstrate that the ensemble agreement is highly correlated with the segmentation errors. Therefore, our method provides measures that can guide local user corrections. To the best of our knowledge, this work is the first ensemble of 3D CNNs for suggesting annotations within images. Our quasi-dense architecture allows the efficient propagation of gradients during training, while limiting the number of parameters, requiring one order of magnitude less parameters than popular medical image segmentation networks such as 3D U-Net (Çiçek, et al.). We also investigated the impact that early or late fusions of multiple image modalities might have on the performances of deep architectures. We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.

A variety of environmental analysis applications have been advanced by the use of satellite remote sensing. Smoke detection based on satellite imagery is imperative for wildfire detection and monitoring. However, the commonly used smoke detection methods mainly focus on smoke discrimination from a few specific classes, which reduces their applicability in different regions of various classes. To this end, in this paper, we present a new large-scale satellite imagery smoke detection benchmark based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, namely USTC_SmokeRS, consisting of 6225 satellite images from six classes (i.e., cloud, dust, haze, land, seaside, and smoke) and covering various areas/regions over the world. To build a baseline for smoke detection in satellite imagery, we evaluate several state-of-the-art deep learning-based image classification models. Moreover, we propose a new convolution neural network (CNN) model, SmokeNet, which incorporates spatial and channel-wise attention in CNN to enhance feature representation for scene classification. The experimental results of our method using different proportions (16%, 32%, 48%, and 64%) of training images reveal that our model outperforms other approaches with higher accuracy and Kappa coefficient. Specifically, the proposed SmokeNet model trained with 64% training images achieves the best accuracy of 92.75% and Kappa coefficient of 0.9130. The model trained with 16% training images can also improve the classification accuracy and Kappa coefficient by at least 4.99% and 0.06, respectively, over the state-of-the-art models.

Background Preoperative discrimination between nonmuscle‐invasive bladder carcinomas (NMIBC) and the muscle‐invasive ones (MIBC) is very crucial in the management of patients with bladder cancer (BC). Purpose To evaluate the discriminative performance of multiparametric MRI radiomics features for precise differentiation of NMIBC from MIBC, preoperatively. Study Type Retrospective, radiomics. Population Fifty‐four patients with postoperative pathologically proven BC lesions (24 in NMIBC and 30 in MIBC groups) were included. Field Strength/Sequence 3.0T MRI/T 2 ‐weighted (T 2 W) and multi‐b‐value diffusion‐weighted (DW) sequences. Assessment A total of 1104 radiomics features were extracted from carcinomatous regions of interest on T 2 W and DW images, and the apparent diffusion coefficient maps. Support vector machine with recursive feature elimination (SVM‐RFE) and synthetic minority oversampling technique (SMOTE) were used to construct an optimal discriminative model, and its performance was evaluated and compared with that of using visual diagnoses by experts. Statistical Tests Chi‐square test and Student's t ‐test were applied on clinical characteristics to analyze the significant differences between patient groups. Results Of the 1104 features, an optimal subset involving 19 features was selected from T 2 W and DW sequences, which outperformed the other two subsets selected from T 2 W or DW sequence in muscle invasion discrimination. The best performance for the differentiation task was achieved by the SVM‐RFE+SMOTE classifier, with averaged sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic of 92.60%, 100%, 96.30%, and 0.9857, respectively, which outperformed the diagnostic accuracy by experts. Data Conclusion The proposed radiomics approach has potential for the accurate differentiation of muscle invasion in BC, preoperatively. The optimal feature subset selected from multiparametric MR images demonstrated better performance in identifying muscle invasiveness when compared with that from T 2 W sequence or DW sequence only. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1489–1498.

Abstract: Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.

Recently, significant progress has been made on semantic segmentation. However, the success of supervised semantic segmentation typically relies on a large amount of labeled data, which is time-consuming and costly to obtain. Inspired by the success of semi-supervised learning methods for image classification, here we propose a simple yet effective semi-supervised learning framework for semantic segmentation. We demonstrate that the devil is in the details: a set of simple designs and training techniques can collectively improve the performance of semi-supervised semantic segmentation significantly. Previous works [3], [25] fail to effectively employ strong augmentation in pseudo-label learning, as the large distribution disparity caused by strong augmentation harms the batch nor-malization statistics. We design a new batch normalization, namely distribution-specific batch normalization (DSBN) to address this problem and show the importance of strong augmentation for semantic segmentation. Moreover, we design a self-correction loss, which is effective in terms of noise resistance. We conduct a series of ablation studies to show the effectiveness of each component. Our method achieves state-of-the-art results in the semi-supervised settings on the Cityscapes and Pascal VOC datasets.

The combined need for advanced treatment of fluoridated water and low-energy technologies has inspired researchers to develop more efficient and green methods of fluoride removal. Capacitive deionization (CDI), a novel water treatment technology, has garnered significant attention in the defluorination field due to its exceptional ion selectivity. Recent decades, CDI has achieved remarkable advancements in electrode materials, F− storage mechanisms, and the potential for engineering applications in the realm of F− removal. However, there has been a lack of reviews that highlight design strategies for electrode materials in terms of fluoride ion properties in water and provide insights into key factors for designing CDI structures with excellent performance and promise for scale applications. Herein, starting from the background of fluorine pollution in surface water, and the selective removal of fluorine heavily relies on the crucial role played by electrode materials. Accordingly, all kinds of defluorinated electrode materials are reviewed. Secondly, the defluorination mechanism is summarized especially the distinctions among these processes. Additionally, the challenges existing in the promotion of CDI defluorination to large-scale applications are firstly analyzed in depth. Finally, future research opportunities and prospects by CDI for defluorination are proposed. In summary, CDI shows great potential for removing fluoride ions from water, offering a fresh approach to sustainable water resource management and environmental protection.

Multi-class labeling is one of the core problems in image analysis. We show how this combinatorial problem can be approximately solved using tools from convex optimization. We suggest a novel functional based on a multidimensional total variation formulation, allowing for a broad range of data terms. Optimization is carried out in the operator splitting framework using Douglas-Rachford Splitting. In this connection, we compare two methods to solve the Rudin-Osher-Fatemi type subproblems and demonstrate the performance of our approach on single- and multichannel images.

Purpose To technically investigate the non-Gaussian diffusion of head and neck diffusion weighted imaging (DWI) at 3 Tesla and compare advanced non-Gaussian diffusion models, including diffusion kurtosis imaging (DKI), stretched-exponential model (SEM), intravoxel incoherent motion (IVIM) and statistical model in the patients with nasopharyngeal carcinoma (NPC). Materials and Methods After ethics approval was granted, 16 patients with NPC were examined using DWI performed at 3T employing an extended b-value range from 0 to 1500 s/mm2. DWI signals were fitted to the mono-exponential and non-Gaussian diffusion models on primary tumor, metastatic node, spinal cord and muscle. Non-Gaussian parameter maps were generated and compared to apparent diffusion coefficient (ADC) maps in NPC. Results Diffusion in NPC exhibited non-Gaussian behavior at the extended b-value range. Non-Gaussian models achieved significantly better fitting of DWI signal than the mono-exponential model. Non-Gaussian diffusion coefficients were substantially different from mono-exponential ADC both in magnitude and histogram distribution. Conclusion Non-Gaussian diffusivity in head and neck tissues and NPC lesions could be assessed by using non-Gaussian diffusion models. Non-Gaussian DWI analysis may reveal additional tissue properties beyond ADC and holds potentials to be used as a complementary tool for NPC characterization.

Ranking residential real estates based on investment values can provide decision making support for home buyers and thus plays an important role in estate marketplace. In this paper, we aim to develop methods for ranking estates based on investment values by mining users' opinions about estates from online user reviews and offline moving behaviors (e.g., Taxi traces, smart card transactions, check-ins). While a variety of features could be extracted from these data, these features are Interco related and redundant. Thus, selecting good features and integrating the feature selection into the fitting of a ranking model are essential. To this end, in this paper, we first strategically mine the fine-grained discrminative features from user reviews and moving behaviors, and then propose a probabilistic sparse pair wise ranking method for estates. Specifically, we first extract the explicit features from online user reviews which express users' opinions about point of interests (POIs) near an estate. We also mine the implicit features from offline moving behaviors from multiple perspectives (e.g., Direction, volume, velocity, heterogeneity, topic, popularity, etc.). Then we learn an estate ranking predictor by combining a pair wise ranking objective and a sparsity regularization in a unified probabilistic framework. And we develop an effective solution for the optimization problem. Finally, we conduct a comprehensive performance evaluation with real world estate related data, and the experimental results demonstrate the competitive performance of both features and the proposed model.

We propose a novel global optimization-based approach to segmentation of 3-D prostate transrectal ultrasound (TRUS) and T2 weighted magnetic resonance (MR) images, enforcing inherent axial symmetry of prostate shapes to simultaneously adjust a series of 2-D slice-wise segmentations in a "global" 3-D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coherent continuous max-flow model (CCMFM), which derives a new and efficient duality-based algorithm, leading to a GPU-based implementation to achieve high computational speeds. Experiments with 25 3-D TRUS images and 30 3-D T2w MR images from our dataset, and 50 3-D T2w MR images from a public dataset, demonstrate that the proposed approach can segment a 3-D prostate TRUS/MR image within 5-6 s including 4-5 s for initialization, yielding a mean Dice similarity coefficient of 93.2%±2.0% for 3-D TRUS images and 88.5%±3.5% for 3-D MR images. The proposed method also yields relatively low intra- and inter-observer variability introduced by user manual initialization, suggesting a high reproducibility, independent of observers.

We propose a novel multi-region image segmentation approach to extract myocardial scar tissue from 3-D whole-heart cardiac late-enhancement magnetic resonance images in an interactive manner. For this purpose, we developed a graphical user interface to initialize a fast max-flow-based segmentation algorithm and segment scar accurately with progressive interaction. We propose a partially-ordered Potts (POP) model to multi-region segmentation to properly encode the known spatial consistency of cardiac regions. Its generalization introduces a custom label/region order constraint to Potts model to multi-region segmentation. The combinatorial optimization problem associated with the proposed POP model is solved by means of convex relaxation, for which a novel multi-level continuous max-flow formulation, i.e., the hierarchical continuous max-flow (HMF) model, is proposed and studied. We demonstrate that the proposed HMF model is dual or equivalent to the convex relaxed POP model and introduces a new and efficient hierarchical continuous max-flow based algorithm by modern convex optimization theory. In practice, the introduced hierarchical continuous max-flow based algorithm can be implemented on the parallel GPU to achieve significant acceleration in numerics. Experiments are performed in 50 whole heart 3-D LE datasets, 35 with left-ventricular and 15 with right-ventricular scar. The experimental results are compared to full-width-at-half-maximum and Signal-threshold to reference-mean methods using manual expert myocardial segmentations and operator variabilities and the effect of user interaction are assessed. The results indicate a substantial reduction in image processing time with robust accuracy for detection of myocardial scar. This is achieved without the need for additional region constraints and using a single optimization procedure, substantially reducing the potential for error.

The advance of mobile technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data streams. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions ) from trajectory data streams. Such technique has broad applications in the areas of scientific study, transportation management, and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the efficiency of algorithms. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery from trajectory streams. The traveling buddies are microgroups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along the trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. In addition, we extend the proposed framework to discover companions on more complicated scenarios with spatial and temporal constraints, such as on the road network and battlefield. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. Experimental results show that our proposed buddy-based approach is an order of magnitude faster than the baselines and achieves higher accuracy in companion discovery.

Precise segmentation of bladder walls and tumor regions is an essential step toward noninvasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of patients with bladder cancer (BC). However, the automatic delineation of bladder walls and tumor in magnetic resonance images (MRI) is a challenging task, due to important bladder shape variations, strong intensity inhomogeneity in urine, and very high variability across the population, particularly on tumors' appearance. To tackle these issues, we propose to leverage the representation capacity of deep fully convolutional neural networks.The proposed network includes dilated convolutions to increase the receptive field without incurring extra cost or degrading its performance. Furthermore, we introduce progressive dilations in each convolutional block, thereby enabling extensive receptive fields without the need for large dilation rates. The proposed network is evaluated on 3.0T T2-weighted MRI scans from 60 pathologically confirmed patients with BC.Experiments show the proposed model to achieve a higher level of accuracy than state-of-the-art methods, with a mean Dice similarity coefficient of 0.98, 0.84, and 0.69 for inner wall, outer wall, and tumor region segmentation, respectively. These results represent a strong agreement with reference contours and an increase in performance compared to existing methods. In addition, inference times are less than a second for a whole three-dimensional (3D) volume, which is between two and three orders of magnitude faster than related state-of-the-art methods for this application.We showed that a CNN can yield precise segmentation of bladder walls and tumors in BC patients on MRI. The whole segmentation process is fully automatic and yields results similar to the reference standard, demonstrating the viability of deep learning models for the automatic multiregion segmentation of bladder cancer MRI images.

Preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients.To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk.Retrospective.Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21).3.0T MRI/T2 -weighted (T2 W), multi-b-value diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) sequences.Radiomics features were extracted from the T2 W, DW, apparent diffusion coefficient, and DCE images. A Rad_Score model was constructed using the support vector machine-based recursive feature elimination approach and a logistic regression model. Combined with the important clinical factors, including age, gender, grade, and muscle-invasive status (MIS) of the archived lesion, tumor size and number, surgery, and image signs like stalk and submucosal linear enhancement, a radiomics-clinical nomogram was developed, and its performance was evaluated in the training and the validation cohorts. The potential clinical usefulness was analyzed by the decision curve.Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction.Of the 1872 features, the 32 with the highest area under the curve (AUC) of receiver operating characteristic were selected for the Rad_Score calculation. The nomogram developed by two independent predictors, MIS and Rad_Score, showed good performance in the training (accuracy 88%, AUC 0.915, P << 0.01) and validation cohorts (accuracy 80.95%, AUC 0.838, P = 0.009). The decision curve exhibited when the risk threshold was larger than 0.3, more benefit was observed by using the radiomics-clinical nomogram than using the radiomics or clinical model alone.The proposed radiomics-clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence.3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1893-1904.

The aim of this study was to explore the feasibility and repeatability of amide proton transfer-weighted (APTw) MRI for the head and neck on clinical MRI scanners. Six healthy volunteers and four patients with head and neck tumors underwent APTw MRI scanning at 3 T. The APTw signal was quantified by the asymmetric magnetization transfer ratio (MTRasym) at 3.5 ppm. Z spectra of normal tissues in the head and neck (masseter muscle, parotid glands, submandibular glands and thyroid glands) were analyzed in healthy volunteers. Inter-scan repeatability of APTw MRI was evaluated in six healthy volunteers. Z spectra of patients with head and neck tumors were produced and APTw signals in these tumors were analyzed. APTw MRI scanning was successful for all 10 subjects. The parotid glands showed the highest APTw signal (~7.6% average), whereas the APTw signals in other tissues were relatively moderate. The repeatability of APTw signals from the masseter muscle, parotid gland, submandibular gland and thyroid gland of healthy volunteers was established. Four head and neck tumors showed positive mean APTw ranging from 1.2% to 3.2%, distinguishable from surrounding normal tissues. APTw MRI was feasible for use in the head and neck regions at 3 T. The preliminary results on patients with head and neck tumors indicated the potential of APTw MRI for clinical applications.

The intestinal microbiota plays crucial roles in the nutrition uptake and metabolism in a herbivorous fish, the grass carp (Ctenopharyngodon idella). Based on the 16S rRNA amplicon data, Firmicutes and Proteobacteria were the two predominant phyla along the intestine; bacterial richness and Shannon diversity index were higher in the middle-intestine than in the fore- and hind-intestine of the grass carp. A significant reduction in Halomonas and Shewanella number was observed in the middle- and hind-intestine as compared with that in the fore intestine; in contrast, a higher relative abundance of Bacteroides, Erysipelotrichaceae, and Cetobacterium was observed in the hind-intestine than in the fore- and middle-intestine. Microbiota located in the fore-, middle-, and hind-intestine formed a unique ecological network model of interspecific interactions. The dominant intestinal microbiota was a major component of the network, and many operational taxonomic units from the dominant microbiota served as connectors and module hubs in the network, which contributed to the homeostasis of the intestinal microbiota. Competitive interactions predominated in the microbial community in the fore- and middle-intestine, whereas cooperative interactions were dominant in the hind-intestine. Predicted function analysis (PICRUSt) showed that microbial function composition significantly changed along the intestine. In the nutrition metabolism, the carbohydrate metabolism functions of the microbiota were increased along the intestine, but the opposite trend was detected in lipid metabolism. The microbiota in the middle-intestine played a more important role in amino acid and energy metabolism, while the hind-intestine was the main site for fiber fermentation, where the predominant positive interactions of microbiota could enhance the ability of the grass carp to obtain more nutrients and energy from plants. Generally, our results suggested that the microbial competition significantly varied along the intestine, as well as the microbial function composition, and complex interspecific interactions promoted the carbohydrate fermentation of intestinal microbiota of grass carp.

Purpose This work is to develop a novel image reconstruction method from highly undersampled multichannel acquisition to reduce the scan time of exponential parameterization of T2 relaxation. Theory and Methods On top of the low‐rank and joint‐sparsity constraints, we propose to exploit the linear predictability of the T2 exponential decay to further improve the reconstruction of the T2‐weighted images from undersampled acquisitions. Specifically, the exact rank prior (i.e., number of non‐zero singular values) is adopted to enforce the spatiotemporal low rankness, while the mixed L2–L1 norm of the wavelet coefficients is used to promote joint sparsity, and the Hankel low‐rank approximation is used to impose linear predictability, which integrates the exponential behavior of the temporal signal into the reconstruction process. An efficient algorithm is adopted to solve the reconstruction problem, where corresponding nonlinear filtering operations are performed to enforce corresponding priors in an iterative manner. Results Both simulated and in vivo datasets with multichannel acquisition were used to demonstrate the feasibility of the proposed method. Experimental results have shown that the newly introduced linear predictability prior improves the reconstruction quality of the T2‐weighted images and benefits the subsequent T2 mapping by achieving high‐speed, high‐quality T2 mapping compared with the existing fast T2 mapping methods. Conclusion This work proposes a novel fast T2 mapping method integrating the linear predictable property of the exponential decay into the reconstruction process. The proposed technique can effectively improve the reconstruction quality of the state‐of‐the‐art fast imaging method exploiting image sparsity and spatiotemporal low rankness. Magn Reson Med 76:1865–1878, 2016. © 2016 International Society for Magnetic Resonance in Medicine

Purpose To evaluate the utility of amide proton transfer (APT) imaging in the characterization of head and neck tumors. Materials and Methods This retrospective study of APT imaging included 117 patients with 70 nasopharyngeal undifferentiated carcinomas (NUCs), 26 squamous cell carcinomas (SCCs), eight non-Hodgkin lymphomas (NHLs), and 13 benign salivary gland tumors (BSGTs). Normal tissues were examined in 25 patients. The APT means of malignant tumors, normal tissues, and benign tumors were calculated and compared with the Student t test and analysis of variance. The added value of the mean APT to the mean apparent diffusion coefficient (ADC) for differentiating malignant and benign tumors was evaluated by using receiver operating characteristic analysis and integrated discrimination index. Results The mean APT of malignant tumors (2.40% ± 0.97 [standard deviation]) was significantly higher than that of brain tissue (1.13% ± 0.43), muscle tissue (0.23% ± 0.73), and benign tumors (1.32% ± 1.20) (P < .001). There were no differences between malignant groups (NUC, 2.37% ± 0.90; SCC, 2.41% ± 1.16; NHL, 2.65% ± 0.89; P = .45 to P = .86). The mean ADC of malignant tumors ([0.85 ± 0.17] × 10-3 mm2/sec) was significantly lower than that of benign tumors ([1.46 ± 0.47] × 10-3 mm2/sec) (P = .001). Adding APT to ADC increased the area under the curve from 0.87 to 0.96, with an integrated discrimination index of 7.6% (P = .13). Conclusion These preliminary data demonstrate differences in amide proton transfer (APT) mean of malignant tumors, normal tissues, and benign tumors, although APT mean could not be used to differentiate between malignant tumor groups. APT imaging has the potential to be of added value to apparent diffusion coefficient in differentiating malignant from benign tumors.

Magnetic resonance guided radiotherapy (MRgRT), enabled by the clinical introduction of the integrated MRI and linear accelerator (MR-LINAC), is a novel technique for prostate cancer (PCa) treatment, promising to further improve clinical outcome and reduce toxicity. The role of prostate MRI has been greatly expanded from the traditional PCa diagnosis to also PCa screening, treatment and surveillance. Diagnostic prostate MRI has been relatively familiar in the community, particularly with the development of Prostate Imaging - Reporting and Data System (PI-RADS). But, on the other hand, the use of MRI in the emerging clinical practice of PCa MRgRT, which is substantially different from that in PCa diagnosis, has been so far sparsely presented in the medical literature. This review attempts to give a comprehensive overview of MRI acquisition techniques currently used in the clinical workflows of PCa MRgRT, from treatment planning to online treatment guidance, in order to promote MRI practice and research for PCa MRgRT. In particular, the major differences in the MRI acquisition of PCa MRgRT from that of diagnostic prostate MRI are demonstrated and explained. Limitations in the current MRI acquisition for PCa MRgRT are analyzed. The future developments of MRI in the PCa MRgRT are also discussed.

It is anticipated that a focal boost to intraprostatic lesions (IPLs) using external beam radiotherapy (EBRT) guided by magnetic resonance imaging (MRI) will increase biochemical disease-free survival (bDFS) without increasing toxicity in the treatment of localized prostate cancer (PC). To systematically review clinical outcomes for MRI-guided EBRT focal boost to IPLs. Three independent reviewers conducted literature searches in three databases according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. The inclusion criteria were original English-language articles from 2000 to 2021 on prospective studies of patients with localized PC (n > 10) receiving an MRI-guided EBRT focal boost to IPLs. The main outcomes and measures were safety, gastrointestinal (GI)/genitourinary (GU) toxicities, quality of life, and biochemical disease outcomes. Weighted random-effects meta-analyses were conducted. Heterogeneity was assessed using the I2 statistic. Publication bias was assessed via funnel plots. Seventeen studies (1290 patients) were included. There were heterogeneities in patient risk category (low risk, 63; intermediate risk, 532; high risk, 695), MRI utilization, and treatment planning and delivery. All studies reported good safety, with estimated rates of 7.5%/7.0% (95% confidence interval [CI] 4.0–12.1%/2.8–12.8%) and 0.1%/0.2% (95% CI 0–0.4%/0–1.1%) for acute/late cumulative grade ≥2 and grade ≥3 gastrointestinal toxicities, and 29.5%/16.0% (95% CI 17.6–43.0%/8.3–25.7%) and 0.4%/1.3% (95% CI 0.0–1.3%/0.3–3.0%) for acute/late grade ≥2 and grade ≥3 genitourinary toxicities, respectively. Across patients in focal boost studies with follow-up >5 yr, bDFS was 92.4% (95% CI 84.5–97.7%). The overall bDFS was 95.0% (95% CI 91.9–97.4%) regardless of follow-up duration. MRI-guided EBRT focal boost to IPLs in localized PC was feasible and safe, with low GI/GU toxicities and favorable biochemical disease outcomes. Level 1 evidence supports the superior bDFS of this approach over whole-prostate irradiation for standard fractionation; however, further research is required for hypofractionation and ultra-hypofractionation. We reviewed 17 studies on the use of magnetic resonance imaging (MRI)-guided radiotherapy with delivery of a higher radiation level to lesions within the prostate that were visible on MRI. This approach was well tolerated and might offer better disease control in prostate cancer over traditional radiotherapy.

With curcumin and doxorubicin (DOX) base as model drugs, intracellular delivery of hydrophobic anticancer drugs by hollow structured superparamagnetic iron oxide (SPIO) nanoshells (hydrodynamic diameter: 191.9 ± 2.6 nm) was studied in glioblastoma U-87 MG cells. SPIO nanoshell-based encapsulation provided a stable aqueous dispersion of the curcumin. After the SPIO nanoshells were internalized by U-87 MG cells, they localized at the acidic compartments of endosomes and lysosomes. In endosome/lysosome-mimicking buffers with a pH of 4.5–5.5, pH-dependent drug release was observed from curcumin or DOX loaded SPIO nanoshells (curcumin/SPIO or DOX/SPIO). Compared with the free drug, the intracellular curcumin content delivered via curcumin/SPIO was 30 fold higher. Increased intracellular drug content for DOX base delivered via DOX/SPIO was also confirmed, along with a fast intracellular DOX release that was attributed to its protonation in the acidic environment. DOX/SPIO enhanced caspase-3 activity by twofold compared with free DOX base. The concentration that induced 50% cytotoxic effect (CC50) was 0.05 ± 0.03 μg ml−1 for DOX/SPIO, while it was 0.13 ± 0.02 μg ml−1 for free DOX base. These results suggested SPIO nanoshells might be a promising intracellular carrier for hydrophobic anticancer drugs.

A fundamental step in the diagnosis of cardiovascular diseases, automatic left ventricle (LV) segmentation in cardiac magnetic resonance images (MRIs) is still acknowledged to be a difficult problem. Most of the existing algorithms require either extensive training or intensive user inputs. This study investigates fast detection of the left ventricle (LV) endo- and epicardium surfaces in cardiac MRI via convex relaxation and distribution matching. The algorithm requires a single subject for training and a very simple user input, which amounts to a single point (mouse click) per target region (cavity or myocardium). It seeks cavity and myocardium regions within each 3D phase by optimizing two functionals, each containing two distribution-matching constraints: (1) a distance-based shape prior and (2) an intensity prior. Based on a global measure of similarity between distributions, the shape prior is intrinsically invariant with respect to translation and rotation. We further introduce a scale variable from which we derive a fixed-point equation (FPE), thereby achieving scale-invariance with only few fast computations. The proposed algorithm relaxes the need for costly pose estimation (or registration) procedures and large training sets, and can tolerate shape deformations, unlike template (or atlas) based priors. Our formulation leads to a challenging problem, which is not directly amenable to convex-optimization techniques. For each functional, we split the problem into a sequence of sub-problems, each of which can be solved exactly and globally via a convex relaxation and the augmented Lagrangian method. Unlike related graph-cut approaches, the proposed convex-relaxation solution can be parallelized to reduce substantially the computational time for 3D domains (or higher), extends directly to high dimensions, and does not have the grid-bias problem. Our parallelized implementation on a graphics processing unit (GPU) demonstrates that the proposed algorithm requires about 3.87 s for a typical cardiac MRI volume, a speed-up of about five times compared to a standard implementation. We report a performance evaluation over 400 volumes acquired from 20 subjects, which shows that the obtained 3D surfaces correlate with independent manual delineations. We further demonstrate experimentally that (1) the performance of the algorithm is not significantly affected by the choice of the training subject and (2) the shape description we use does not change significantly from one subject to another. These results support the fact that a single subject is sufficient for training the proposed algorithm.

Objective To study the CT and MR features of xanthogranulomatous cholecystitis (XGC). Materials and methods 49 patients had pathologically confirmed XGC. All patients underwent contrast enhanced CT, and 10 patients had additional plain MRI. The CT and MRI results were retrospectively analyzed. Results On CT, all patients had thickening of gallbladder wall, with 87.8% cases showed diffuse thickening. 85.7% cases had intramural hypo-attenuated nodules in the thickened wall. Continuous mucosal line and luminal surface enhancement were noted in 79.6% and 85.7% cases, respectively. Gallbladder stones were seen in 69.4% patients. The coexistence of the above 5 CT features was seen in 40% cases, and 80% cases had the coexistence of ≥4 features. Diffused gallbladder wall thickening in XGC is more likely to have disrupted mucosal line, and XGC with disrupted mucosal line is more likely to be associated with liver infiltration. In 60% patients the inflammatory process extended beyond gallbladder, with the interface between gallbladder and liver and/or the surrounding fat blurred. 40% cases had an early enhancement of liver parenchyma. Infiltration to other surrounding tissues included bowel (n = 3), stomach (n = 2), and abdominal wall (n = 1). On MR images, 7 of 9 intramural nodules in 7 subjects with T1-weighted dual echo MR images showed higher signal intensity on in-phase images than out-of-phase images. Conclusion Coexisting of diffuse gallbladder wall thickening, hypo-attenuated intramural nodules, continuous mucosal line, luminal surface enhancement, and gallbladder stone highly suggest XGC. XGC frequently infiltrate liver and surrounding fat. Chemical-shift MRI helps classifying intramural nodules in the gallbladder wall.

Efficient and accurate segmentation of the prostate and two of its clinically meaningful sub-regions: the central gland (CG) and peripheral zone (PZ), from 3D MR images, is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this work, a novel multi-region segmentation approach is proposed to simultaneously segment the prostate and its two major sub-regions from only a single 3D T2-weighted (T2w) MR image, which makes use of the prior spatial region consistency and incorporates a customized prostate appearance model into the segmentation task. The formulated challenging combinatorial optimization problem is solved by means of convex relaxation, for which a novel spatially continuous max-flow model is introduced as the dual optimization formulation to the studied convex relaxed optimization problem with region consistency constraints. The proposed continuous max-flow model derives an efficient duality-based algorithm that enjoys numerical advantages and can be easily implemented on GPUs. The proposed approach was validated using 18 3D prostate T2w MR images with a body-coil and 25 images with an endo-rectal coil. Experimental results demonstrate that the proposed method is capable of efficiently and accurately extracting both the prostate zones: CG and PZ, and the whole prostate gland from the input 3D prostate MR images, with a mean Dice similarity coefficient (DSC) of 89.3±3.2% for the whole gland (WG), 82.2±3.0% for the CG, and 69.1±6.9% for the PZ in 3D body-coil MR images; 89.2±3.3% for the WG, 83.0±2.4% for the CG, and 70.0±6.5% for the PZ in 3D endo-rectal coil MR images. In addition, the experiments of intra- and inter-observer variability introduced by user initialization indicate a good reproducibility of the proposed approach in terms of volume difference (VD) and coefficient-of-variation (CV) of DSC.

T1ρ relaxation time provides a new contrast mechanism that differs from T1- and T2-weighted contrast, and is useful to study low-frequency motional processes and chemical exchange in biological tissues. T1ρ imaging can be performed in the forms of T1ρ-weighted image, T1ρ mapping and T1ρ dispersion. T1ρ imaging, particularly at low spin-lock frequency, is sensitive to B0 and B1 inhomogeneity. Various composite spin-lock pulses have been proposed to alleviate the influence of field inhomogeneity so as to reduce the banding-like spin-lock artifacts. T1ρ imaging could be specific absorption rate (SAR) intensive and time consuming. Efforts to address these issues and speed-up data acquisition are being explored to facilitate wider clinical applications. This paper reviews the T1ρ imaging's basic physic principles, as well as its application for cartilage imaging and intervertebral disc imaging. Compared to more established T2 relaxation time, it has been shown that T1ρ provides more sensitive detection of proteoglycan (PG) loss at early stages of cartilage degeneration. T1ρ has also been shown to provide more sensitive evaluation of annulus fibrosis (AF) degeneration of the discs.

Preterm neonates with a very low birth weight of less than 1,500 g are at increased risk for developing intraventricular hemorrhage (IVH). Progressive ventricle dilatation of IVH patients may cause increased intracranial pressure, leading to neurological damage, such as neurodevelopmental delay and cerebral palsy. The technique of 3D ultrasound (US) imaging has been used to quantitatively monitor the ventricular volume in IVH neonates, which may elucidate the ambiguity surrounding the timing of interventions in these patients as 2D clinical US imaging relies on linear measurement and visual estimation of ventricular dilation from a series of 2D slices. To translate 3D US imaging into the clinical setting, a fully automated segmentation algorithm is necessary to extract the ventricular system from 3D neonatal brain US images. In this paper, an automatic segmentation approach is proposed to delineate lateral ventricles of preterm neonates from 3D US images. The proposed segmentation approach makes use of phase congruency map, multi-atlas initialization technique, atlas selection strategy, and a multiphase geodesic level-sets (MGLS) evolution combined with a spatial shape prior derived from multiple pre-segmented atlases. Experimental results using 30 IVH patient images show that the proposed GPU-implemented approach is accurate in terms of the Dice similarity coefficient (DSC), the mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD). To the best of our knowledge, this paper reports the first study on automatic segmentation of the ventricular system of premature neonatal brains from 3D US images.

MRI can detect early-stage nasopharyngeal carcinoma (NPC), but the detection is more challenging in early-stage NPCs because they must be distinguished from benign hyperplasia in the nasopharynx. This study aimed to determine whether intravoxel incoherent motion diffusion-weighted imaging (IVIM DWI) MRI could distinguish between these two entities. Thirty-four subjects with early-stage NPC and 30 subjects with benign hyperplasia prospectively underwent IVIM DWI. The mean pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) and apparent diffusion coefficient (ADC) values were calculated for all subjects and compared between the 2 groups using Student’s t test. Receiver operating characteristics with the area under the curve (AUC) was used to identify the optimal threshold for all significant parameters, and the corresponding diagnostic performance was calculated. A p value of < 0.05 was considered statistically significant. Compared with benign hyperplasia, early-stage NPC exhibited a significantly lower D mean (0.64 ± 0.06 vs 0.87 ± 0.11 × 10−3 mm2/s), ADC0–1000 mean (0.77 ± 0.08 vs 1.00 ± 0.13 × 10−3 mm2/s), ADC300–1000 (0.63 ± 0.05 vs 0.86 ± 0.10 × 10−3 mm2/s) and a higher D* mean (32.66 ± 4.79 vs 21.96 ± 5.21 × 10−3 mm2/s) (all p < 0.001). No significant difference in the f mean was observed between the two groups (p = 0.216). The D and ADC300–1000 mean had the highest AUC of 0.985 and 0.988, respectively, and the D mean of < 0.75 × 10−3 mm2/s yielded the highest sensitivity, specificity and accuracy (100%, 93.3% and 96.9%, respectively) in distinguishing early-stage NPC from benign hyperplasia. DWI has potential to distinguish early-stage NPC from benign hyperplasia and D and ADC300–1000 mean were the most promising parameters. • Diffusion-weighted imaging has potential to distinguish early-stage nasopharyngeal carcinoma from benign hyperplasia in the nasopharynx. • The pure diffusion coefficient, pseudo-diffusion coefficient from intravoxel incoherent motion model and apparent diffusion coefficient from conventional diffusion-weighted imaging were significant parameters for distinguishing these two entities in the nasopharynx. • The pure diffusion coefficient, followed by apparent diffusion coefficient, may be the most promising parameters to be used in screening studies to help detect early-stage nasopharyngeal carcinoma.

Abstract Purpose The MR‐guided radiotherapy (MRgRT) images on the integrated MRI and linear accelerator (MR‐LINAC) might facilitate radiomics analysis for longitudinal treatment response assessment. However, the reliability of MRgRT radiomics features is largely unknown. This study aims to investigate MRgRT radiomics feature reliability acquired using a standardized 3D‐T2W‐TSE sequence in terms of repeatability, reproducibility, and within‐subject feature agreement on a 1.5T MR‐simulator and a 1.5T MR‐LINAC for prostate cancer (PC). Methods Twenty‐six consecutive PC patients who underwent one MRI‐simulator scan and two MR‐LINAC scans before dose delivery were retrospectively included. The three MRI datasets were rigidly co‐registered. 1023 first‐order and texture radiomics features were extracted with different intensity bin widths for each scan in the manually segmented clinical target volume (CTV) and planning target volume (PTV) by an experienced radiation oncologist. Intraclass correlation coefficient (ICC) was used to evaluate feature repeatability between MR‐LINAC scans and reproducibility between MRI‐simulator and MR‐LINAC scans. The within‐subject feature value agreements were evaluated using Bland–Altman analysis. The impact of inter‐observer segmentation on the radiomics feature reliability was also examined based on the second manual segmentation of CTV and PTV by an MRI researcher. Results Based on the segmentation by the radiation oncologist and the default bin width of 25, 9.6%, 24.1%, 49.6%, and 16.8% of the total 1023 features exhibited excellent (ICC &gt; 0.9), good (0.9 &gt; ICC &gt; 0.75), moderate (0.75 &gt; ICC &gt; 0.5), and poor (ICC &lt; 0.5) repeatability in the CTV, and 9.2%, 26.8%, 50.5%, and 13.5% in the PTV, respectively. For reproducibility, the corresponding feature percentages were 8.9%, 19.7%, 41.9%, and 29.6% in the CTV, and 8.4%, 17.8%, 47.9%, and 26% in the PTV. Feature reliability was not notably influenced by intensity bin width for discretization. BA analysis revealed wide 95% limit‐of‐agreements and substantial biases of feature values between CTV and PTV and between any two MRI scans. The features even with excellent ICC were still subjected to considerable inter‐scan feature variations in each individual subject. The analysis on the second segmentation by the MRI researcher showed insignificantly different feature repeatability and reproducibility in terms of ICC values. Conclusions Only a small proportion of features exhibited excellent/good repeatability and reproducibility, highlighting the importance of reliable MRgRT feature selection. The within‐subject feature values were subjected to considerable inter‐scan variations, imposing a challenge on the determination of the smallest detectable change in future MRgRT delta‐radiomics studies.

Purpose The paper aims to propose a virtual reality (VR) wisdom teaching model in open university English course from the perspective of “Metaverse”. The study aims to testify the stimulation for English learning and the effectiveness of English-expressing with VR tools for adult learners from the practice in a pilot reform project. Design/methodology/approach The paper opted for an exploratory study using ICARE Design Model as the framework, under the grounded theories of constructivism and multi-modal teaching. The study compared the evaluation data of one-semester English learning performance between the experimental class (67 students) with VR practice and the controlled class (67 students), including speaking test score, qualitative feedback and in-depth experience analysis. The data were complemented by reflection paper analysis, including manual evaluation (the criteria of semantics, pronunciation, fluency and completeness), questionnaire survey (in the form of five-point Likert scale) and semi-structured interview. Findings The paper provides empirical insights about the VR wisdom teaching model in English language teaching and learning in a Chinese Open University. The empirical results suggest that “3I” features of VR technology could make up for the shortcomings of traditional English classes in open universities in China, and VR resources designed with curriculum teaching materials could also be helpful for students’ command of knowledge points and language skills. What’s more, the sense of authentic experience in virtual could promote the teaching and learning effect in college English classes. Research limitations/implications The present study focuses on a wisdom mode of foreign language teaching and learning for adult learners in open education, so the research results may lack generalizability. Therefore, researchers are encouraged to further explore the deep integration of VR/artificial intelligence in foreign language teaching and learning. Originality/value This paper fulfills an identified need to study how VR tools provide an engaging, fun and immersive language learning environment, to enhance autonomous learning and learning engagement.

Gas adsorption remains an attractive area of research. The hierarchical structure can reduce diffusion limitations and facilitate molecular transport, while acid sites can be used as adsorption sites. These make zeolites widely used in the field of gas adsorption. How to obtain zeolite adsorbents with better adsorption properties by modulating the hierarchical structure and acid sites is a pressing issue nowadays. This review highlights the strategies to modulate the hierarchical structure as well as the acid sites; and then explains how these strategies are achieved. The mechanism of zeolite adsorption on gases is then described in terms of these two properties. Lastly, the adsorption properties of zeolites for certain gases under specific conditions are summarised. An outlook of zeolite hierarchical structures and acid site modulation strategies is given.

Objectives: The aim of this study was to explore the technical feasibility of T 1 r MRI for the liver, and to determine the normal range of liver T 1 r in healthy subjects at clinical 3 T. Methods: There were 15 healthy volunteers.Three representative axial slices were selected to cut through the upper, middle and lower liver.A rotary echo spin-lock pulse was implemented in a two-dimensional fast-field echo sequence.Spin-lock frequency was 500 Hz, and the spin-lock times of 1, 10, 20, 30, 40 and 50 ms were used for T 1 r mapping.The images were acquired slice by slice during breath-holding.Regions of interest (ROIs; n55) were manually placed on each slice of the liver parenchyma region, excluding artefacts and vessels.The mean value of these ROIs (n515) was regarded as the liver T 1 r value for the subject.Six subjects were scanned once at fasting status; six subjects were scanned once 2 h post meal; three subjects were scanned twice at fasting status; and seven subjects were scanned twice 2 h post meal.Results: When two readers measured the same 10 data sets, the interreader reproducibility (ICC: intraclass correlation coefficient) was 0.955.With the 10 subjects scanned twice, the ICC for scan-rescan reproducibility was 0.764.There was no significant difference for the liver T 1 r value at the fasting status (43.08¡1.41ms) and post-meal status (42.97¡2.38 ms, p50.867).Pooling together all the 32 scans in this study, the normal liver T 1 r value ranged from 38.6 to 48.3 ms (mean 43.0 ms, median 42.6 ms).Conclusion: It is feasible to obtain consistent liver T 1 r measurement for human subjects at 3 T.

In this paper, we propose a novel global optimization based 3-D multi-region segmentation algorithm for T1-weighted black-blood carotid magnetic resonance (MR) images. The proposed algorithm partitions a 3-D carotid MR image into three regions: wall, lumen, and background. The algorithm performs such partitioning by simultaneously evolving two coupled 3-D surfaces of carotid artery adventitia boundary (AB) and lumen-intima boundary (LIB) while preserving their anatomical inter-surface consistency such that the LIB is always located within the AB. In particular, we show that the proposed algorithm results in a fully time implicit scheme that propagates the two linearly ordered surfaces of the AB and LIB to their globally optimal positions during each discrete time frame by convex relaxation. In this regard, we introduce the continuous max-flow model and prove its duality/equivalence to the convex relaxed optimization problem with respect to each evolution step. We then propose a fully parallelized continuous max-flow-based algorithm, which can be readily implemented on a GPU to achieve high computational efficiency. Extensive experiments, with four users using 12 3T MR and 26 1.5T MR images, demonstrate that the proposed algorithm yields high accuracy and low operator variability in computing vessel wall volume. In addition, we show the algorithm outperforms previous methods in terms of high computational efficiency and robustness with fewer user interactions.

Long scanning time greatly hinders the widespread application of spin-lattice relaxation in rotating frame (T1ρ) in clinics. In this study, a novel method is proposed to reconstruct the T1ρ-weighted images from undersampled k-space data and hence accelerate the acquisition of T1ρ imaging.The proposed approach (PANDA-T1ρ) combined the benefit of PCA and dictionary learning when reconstructing image from undersampled data. Specifically, the PCA transform was first used to sparsify the image series along the parameter direction and then the sparsified images were reconstructed by means of dictionary learning and finally solved the images. A variation of PANDA-T1ρ was also developed for the heavy noise case. Numerical simulation and in vivo experiments were carried out with the accelerating factor from 2 to 4 to verify the performance of PANDA-T1ρ.The reconstructed T1ρ maps using the PANDA-T1ρ method were found to be comparable to the reference at all verified acceleration factors. Moreover, the variation exhibited better performance than the original version when the k-space data were contaminated by heavy noise.PANDA-T1ρ can significantly reduce the scanning time of T1ρ by integrating PCA and dictionary learning and provides better parameter estimation than the state-of-art methods for a fixed acceleration factor.

This study was aimed to determine whether pure molecular-based diffusion coefficient (D) and perfusion-related diffusion parameters (perfusion fraction f, perfusion-related diffusion coefficient D*) differ in healthy livers and fibrotic livers through intra-voxel incoherent motion (IVIM) MR imaging.17 healthy volunteers and 34 patients with histopathologically confirmed liver fibrosis patients (stage 1 = 14, stage 2 = 8, stage 3 & 4 = 12, METAVIR grading) were included. Liver MR imaging was performed at 1.5-T. IVIM diffusion weighted imaging sequence was based on standard single-shot DW spin echo-planar imaging, with ten b values of 10, 20, 40, 60, 80, 100, 150, 200, 400, 800 sec/mm2 respectively. Pixel-wise realization and regions-of-interest based quantification of IVIM parameters were performed.D, f, and D* in healthy volunteer livers and patient livers were 1.096±0.155 vs 0.917±0.152 (10(-3) mm2/s, p = 0.0015), 0.164±0.021 vs 0.123±0.029 (p<0.0001), and 13.085±2.943 vs 9.423±1.737 (10(-3) mm2/s, p<0.0001) respectively, all significantly lower in fibrotic livers. As the fibrosis severity progressed, D, f, and D* values decreased, with a trend significant for f and D*.Fibrotic liver is associated with lower pure molecular diffusion, lower perfusion volume fraction, and lower perfusion-related diffusion. The decrease of f and D* in the liver is significantly associated liver fibrosis severity.

Accurate representation of myocardial infarct geometry is crucial to patient-specific computational modeling of the heart in ischemic cardiomyopathy. We have developed a methodology for segmentation of left ventricular (LV) infarct from clinically acquired, two-dimensional (2D), late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, for personalized modeling of ventricular electrophysiology. The infarct segmentation was expressed as a continuous min-cut optimization problem, which was solved using its dual formulation, the continuous max-flow (CMF). The optimization objective comprised of a smoothness term, and a data term that quantified the similarity between image intensity histograms of segmented regions and those of a set of training images. A manual segmentation of the LV myocardium was used to initialize and constrain the developed method. The three-dimensional geometry of infarct was reconstructed from its segmentation using an implicit, shape-based interpolation method. The proposed methodology was extensively evaluated using metrics based on geometry, and outcomes of individualized electrophysiological simulations of cardiac dys(function). Several existing LV infarct segmentation approaches were implemented, and compared with the proposed method. Our results demonstrated that the CMF method was more accurate than the existing approaches in reproducing expert manual LV infarct segmentations, and in electrophysiological simulations. The infarct segmentation method we have developed and comprehensively evaluated in this study constitutes an important step in advancing clinical applications of personalized simulations of cardiac electrophysiology.

Objective To study the CT characteristics of solitary focal organizing pneumonia (FOP). Materials and methods Chest CT of consecutive 45 patients (34 males and 11 females, median age: 56 years) with confirmed FOP were analyzed. The CT features between large FOP (>3 cm, n = 27) and small FOP (≤3 cm, n = 18) were compared. Results FOP lesions predominately located in peripheral lungs (86.7%), with the right lower lobe being most common lobe (44.4%). No lesion mainly located in the inner 1/3 of lungs. All large lesions were polygon in shape and had an irregular margin, while small lesions were more likely to be round or oval with an irregular or smooth border. Air bronchogram or small bubble-like lucency was present in majority of the lesions. 42.2% of lesions had incompact internal structure with inhomogeneous density besides air component. Most lesions were associated with a contraction or convergence of surrounding vessels; while no pulmonary vessel was interrupted abruptly by a small FOP lesion. Majority of large lesions had broad contact with the pleura, while only one patient had mild pleural effusion. Mild mediastinal lymph nodes enlargement was present in about 1/5 of the patients. Conclusion Compared with the known CT features of lung cancer, our results suggest differential diagnosis can often be made for large FOP, while small FOP may resemble lung cancer.

<h3>BACKGROUND AND PURPOSE:</h3> Pretreatment prediction of patients with nasopharyngeal carcinoma who will fail conventional treatment would potentially allow these patients to undergo more intensive treatment or closer posttreatment monitoring. The aim of the study was to determine the ability of pretreatment DWI to predict local failure in patients with nasopharyngeal carcinoma based on long-term clinical outcome. <h3>MATERIALS AND METHODS:</h3> One hundred fifty-eight patients with pretreatment DWI underwent analysis of the primary tumor to obtain the ADC mean, ADC skewness, ADC kurtosis, volume, and T-stage. Univariate and multivariate analyses using logistic regression were performed to compare the ADC parameters, volume, T-stage, and patient age in primary tumors with local failure and those with local control, by using a minimum of 5-year follow-up to confirm local control. <h3>RESULTS:</h3> Local control was achieved in 131/158 (83%) patients (range, 60.3–117.7 months) and local failure occurred in 27/158 (17%) patients (range, 5.2–79.8 months). Compared with tumors with local control, those with local failure showed a significantly lower ADC skewness (ADC values with the greatest frequencies were shifted away from the lower ADC range) (<i>P</i> = .006) and lower ADC kurtosis (curve peak broader) (<i>P</i> = .024). The ADC skewness remained significant on multivariate analysis (<i>P</i> = .044). There was a trend toward higher tumor volumes in local failure, but the volume, together with T-stage and ADC mean, were not significantly different between the 2 groups. <h3>CONCLUSIONS:</h3> Pretreatment DWI of primary tumors found that the skewness of the ADC distribution curve was a predictor of local failure in patients with nasopharyngeal carcinoma, based on long-term clinical outcome.

The association between gut microbiome and chronic metabolic disease including polycystic ovary syndrome (PCOS), is well documented, however, the relationship between the gut microbiota and serum metabolites remains unknown. In this study, untargeted metabolomics together with a 16S rRNA gene sequencing tool was used to detect small molecule serum metabolites and the gut microbiome. We identified 15 differential metabolites between PCOS patients and the healthy control. Lysophosphatidylcholine (LPC) (18:2, 20:3, 18:1, P-16:0, 17:0, 15:0, 18:3, 20:4), phosphatidylcholine(PC), ganglioside GA2 (d18:1/16:0) and 1-linoleoylglycerophosphocholine were increased in the PCOS group, and the concentrations of phosphoniodidous acid, bilirubin, nicotinate beta-D-ribonucleotide and citric acid were decreased in the PCOS group, suggesting a lipid metabolism and energy metabolism disorder in the PCOS patients. The diversity of gut microbiota in PCOS group was lower than that in healthy controls. Escherichia/Shigella, Alistipes and an unnamed strain 0319_6G20 belonging to Proteobacteria were important distinguishing genera (LDA > 3.5) in PCOS. Prevotella_9 was positively correlated with phosphoniodidous acid, nicotinate beta-D-ribonucleotide and citric acid concentrations, and negatively correlated with the concentration of LPC (20:3) and 1-linoleoylglycerophosphocholine; Roseburia was negatively correlated with LPC concentration (20:4), while the characteristic genus 0319_6G20 of PCOS was positively correlated with LPC concentration (20:3) (COR > 0.45). SF-36 in the PCOS group was significantly lower than that in the healthy control (HC) group, which was associated with the presence of Escherichia-Shigella and Alistipes. Our finding demonstrated the correlation between the gut microbiota and serum metabolites in PCOS, and therefore characteristic gut microbiota and metabolites may play an important role in the insulin resistance and the mood changes of PCOS patients.

After the Chinese government put forward carbon peaking and carbon neutrality goals, the intensity of environmental regulation has reached an unprecedented height. Using a sample of heavily polluted A-share listed companies in Shanghai and Shenzhen from 2012 to 2018, we discuss the influence mechanism of environmental regulation and environmental tax on corporate earnings management in this study. We use multiple regression models to empirically verify the impact of environmental regulation, environmental tax, and their combined effect on corporate earnings management. We find that environmental regulations promote enterprises’ upward real earnings management and inhibit enterprises’ upward accrual earnings management. However, environmental taxes discourage firms from upward accrual earnings management. Moreover, environmental regulations and environmental tax jointly promote enterprises’ upward accrual earnings management and real earnings management. And there is heterogeneity among different enterprise natures, different enterprise sizes, enterprises in regions with different degrees of marketization, different intensities of government investment in environmental protection, and whether enterprises disclose their environmental protection concepts. The contribution of this paper is to put environmental regulation, environmental protection tax, and earnings management in the same analytical framework. We aim to combine the government’s macro policy with the enterprise’s micro behavior and to deeply analyze the impact and mechanism of environmental regulation, environmental protection tax, and their combined effect on enterprise earnings management. By analyzing the heterogeneity of these impacts from multiple dimensions, this study tries to expand the research horizon, fill the research gap, and provide theoretical support for the government to formulate comprehensive environmental regulation policies.

Vascular visualization is crucial in monitoring, diagnosing, and treating vascular diseases. Laser speckle contrast imaging (LSCI) is widely used for imaging blood flow in shallow or exposed vessels. However, traditional contrast computation using a fixed-sized sliding window introduces noise. In this paper, we propose dividing the laser speckle contrast image into regions and using the variance criterion to extract pixels more suitable for the corresponding regions for calculation, and changing the shape and size of the analysis window at the vascular boundary regions. Our results show that this method has a higher noise reduction and better image quality in deeper vessel imaging, revealing more microvascular structure information.

Obesity is a complex metabolic disease, with cognitive impairment being an essential complication. Gut microbiota differs markedly between individuals with and without obesity. The microbial–gut–brain axis is an important pathway through which metabolic factors, such as obesity, affect the brain. Probiotics have been shown to alleviate symptoms associated with obesity and neurobehavioral disorders. In this review, we evaluated previously published studies on the effectiveness of probiotic interventions in reducing cognitive impairment, depression, and anxiety associated with obesity or a high-fat diet. Most of the probiotics studied have beneficial health effects on obesity-induced cognitive impairment and anxiety. They positively affect immune regulation, the hypothalamic–pituitary–adrenal axis, hippocampal function, intestinal mucosa protection, and glucolipid metabolism regulation. Probiotics can influence changes in the composition of the gut microbiota and the ratio between various flora. However, probiotics should be used with caution, particularly in healthy individuals. Future research should further explore the mechanisms underlying the gut–brain axis, obesity, and cognitive function while overcoming the significant variation in study design and high risk of bias in the current evidence.

To observe the effect of beinaglutide combined with metformin versus metformin alone on weight loss and metabolic profiles in obese patients with polycystic ovary syndrome(PCOS).A total of 64 overweight/obese women with PCOS diagnosed via the Rotterdam criteria were randomly assigned to metformin(MET) 850 mg twice a day(BID) or combined MET 850 mg BID with beinaglutide (COMB) starting at 0.1mg three times a day(TID)and increasing to 0.2mg TID two weeks later. The main endpoints were changes in anthropometric measurements of obesity. Glucose and lipid metabolic, gonadal profiles, and antral follicle count changes as secondary outcomes were also observed.60(93.75%) patients completed the study. In terms of lowering weight, body mass index (BMI),waist circumference(WC) and waist to height ratio(WHtR), COMB treatment outperformed MET monotherapy. Subjects in the COMB arm lost weight 4.54±3.16kg compared with a 2.47±3.59kg loss in the MET arm. In the COMB group, BMI,WC and WHtR were reduced significantly compared with that in the MET group, respectively. COMB therapy is also more favorable in the reduction of fasting insulin(FINS), total testosterone(TT), and homeostasis model assessment-insulin resistance(HOMA-IR) when compared to MET therapy. Antral follicle count and ovarian volume were non-significantly changed in both groups.The most frequent side effects in both groups were mild and moderate digestive symptoms. Itching and induration at the injection site were reported with COMB treatment.Short-term combined treatment with beinaglutide and metformin appears superior to metformin monotherapy in lowering body weight, BMI, WC,WHtR and improving insulin sensitivity and androgen excess in women with PCOS and obesity, with tolerable adverse events.https://www.chictr.org.cn/listbycreater.aspx, identifier ChiCTR2000033741.

The generalized auto-calibrating partially parallel acquisition (GRAPPA) is an auto-calibrating parallel imaging technique which incorporates multiple blocks of data to derive the missing signals. In the original GRAPPA reconstruction algorithm only the data points in phase encoding direction are incorporated to reconstruct missing points in k-space. It has been recognized that this scheme can be extended so that data points in readout direction are also utilized and the points are selected based on a k-space locality criterion. In this study, an automatic subset selection strategy is proposed which can provide a tailored selection of source points for reconstruction. This novel approach extracts a subset of signal points corresponding to the most linearly independent base vectors in the coefficient matrix of fit, effectively preventing incorporating redundant signals which only bring noise into reconstruction with little contribution to the exactness of fit. Also, subset selection in this way has a regularization effect since the vectors corresponding to the smallest singular values are eliminated and consequently the condition of the reconstruction is improved. Phantom and in vivo MRI experiments demonstrate that this subset selection strategy can effectively improve SNR and reduce residual artifacts for GRAPPA reconstruction.

We study the estimation and decomposition of optical flows from highly nonrigid motions. To this end, recent methods from image decomposition into structural and textural parts are combined with variational optical flow estimation. The approaches we suggest amount to minimizing discrete convex functionals using second-order cone programming. Higher-order regularization is necessary in order to accurately recover important flow structure like vortices, and to incorporate key physical properties such as vanishing divergence. For proper discretization, we apply the finite mimetic difference method, which preserves the identities fulfilled by the continuous differential operators. Numerical examples demonstrate the feasibility of the complex approaches.

Abstract MR thermometry can be a very challenging application, as good resolution may be needed along spatial, temporal, and temperature axes. Given that the heated foci produced during thermal therapies are typically much smaller than the anatomy being imaged, much of the imaged field‐of‐view is not actually being heated and may not require temperature monitoring. In this work, many‐fold improvements were obtained in terms of temporal resolution and/or 3D spatial coverage by sacrificing some of the in‐plane spatial coverage. To do so, three fast‐imaging approaches were jointly implemented with a spoiled gradient echo sequence: (1) two‐dimensional spatially selective RF excitation, (2) unaliasing by Fourier encoding the overlaps using the temporal dimension (UNFOLD), and (3) parallel imaging. The sequence was tested during experiments with focused ultrasound heating in ex vivo tissue and a tissue‐mimicking phantom. Temperature maps were estimated from phase‐difference images based on the water proton resonance frequency shift. Results were compared to those obtained from a spoiled gradient echo sequence sequence, using a t ‐test. Temporal resolution was increased by 24‐fold, with temperature uncertainty less than 1°C, while maintaining accurate temperature measurements (mean difference between measurements, as observed in gel = 0.1°C ± 0.6; R = 0.98; P &gt; 0.05). Magn Reson Med 66:112–122, 2011. © 2011 Wiley‐Liss, Inc.

The advance of object tracking technologies leads to huge volumes of spatio-temporal data accumulated in the form of location trajectories. Such data bring us new opportunities and challenges in efficient trajectory retrieval. In this paper, we study a new type of query that finds the k Nearest Neighboring Trajectories (k-NNT) with the minimum aggregated distance to a set of query points. Such queries, though have a broad range of applications like trip planning and moving object study, cannot be handled by traditional k-NN query processing techniques that only find the neighboring points of an object. To facilitate scalable, flexible and effective query execution, we propose a k-NN trajectory retrieval algorithm using a candidate-generation-and-verification strategy. The algorithm utilizes a data structure called global heap to retrieve candidate trajectories near each individual query point. Then, at the verification step, it refines these trajectory candidates by a lower-bound computed based on the global heap. The global heap guarantees the candidate’s completeness (i.e., all the k-NNTs are included), and reduces the computational overhead of candidate verification. In addition, we propose a qualifier expectation measure that ranks partial-matching candidate trajectories to accelerate query processing in the cases of non-uniform trajectory distributions or outlier query locations. Extensive experiments on both real and synthetic trajectory datasets demonstrate the feasibility and effectiveness of proposed methods.

Purpose: Three‐dimensional ultrasound (3DUS) vessel wall volume (VWV) provides a 3D measurement of carotid artery wall remodeling and atherosclerotic plaque and is sensitive to temporal changes of carotid plaque burden. Unfortunately, although 3DUS VWV provides many advantages compared to measurements of arterial wall thickening or plaque alone, it is still not widely used in research or clinical practice because of the inordinate amount of time required to train observers and to generate 3DUS VWV measurements. In this regard, semiautomated methods for segmentation of the carotid media‐adventitia boundary (MAB) and the lumen‐intima boundary (LIB) would greatly improve the time to train observers and for them to generate 3DUS VWV measurements with high reproducibility. Methods: The authors describe a 3D algorithm based on a modified sparse field level set method for segmenting the MAB and LIB of the common carotid artery (CCA) from 3DUS images. To the authors’ knowledge, the proposed algorithm is the first direct 3D segmentation method, which has been validated for segmenting both the carotid MAB and the LIB from 3DUS images for the purpose of computing VWV. Initialization of the algorithm requires the observer to choose anchor points on each boundary on a set of transverse slices with a user‐specified interslice distance (ISD), in which larger ISD requires fewer user interactions than smaller ISD. To address the challenges of the MAB and LIB segmentations from 3DUS images, the authors integrated regional‐ and boundary‐based image statistics, expert initializations, and anatomically motivated boundary separation into the segmentation. The MAB is segmented by incorporating local region‐based image information, image gradients, and the anchor points provided by the observer. Moreover, a local smoothness term is utilized to maintain the smooth surface of the MAB. The LIB is segmented by constraining its evolution using the already segmented surface of the MAB, in addition to the global region‐based information and the anchor points. The algorithm‐generated surfaces were sliced and evaluated with respect to manual segmentations on a slice‐by‐slice basis using 21 3DUS images. Results: The authors used ISD of 1, 2, 3, 4, and 10 mm for algorithm initialization to generate segmentation results. The algorithm‐generated accuracy and intraobserver variability results are comparable to the previous methods, but with fewer user interactions. For example, for the ISD of 3 mm, the algorithm yielded an average Dice coefficient of 94.4% ± 2.2% and 90.6% ± 5.0% for the MAB and LIB and the coefficient of variation of 6.8% for computing the VWV of the CCA, while requiring only 1.72 min (vs 8.3 min for manual segmentation) for a 3DUS image. Conclusions: The proposed 3D semiautomated segmentation algorithm yielded high‐accuracy and high‐repeatability, while reducing the expert interaction required for initializing the algorithm than the previous 2D methods.

In this study, we proposed an efficient nonrigid magnetic resonance (MR) to transrectal ultrasound (TRUS) deformable registration method in order to improve the accuracy of targeting suspicious regions during a three dimensional (3-D) TRUS guided prostate biopsy. The proposed deformable registration approach employs the multi-channel modality independent neighborhood descriptor (MIND) as the local similarity feature across the two modalities of MR and TRUS, and a novel and efficient duality-based convex optimization-based algorithmic scheme was introduced to extract the deformations and align the two MIND descriptors. The registration accuracy was evaluated using 20 patient images by calculating the TRE using manually identified corresponding intrinsic fiducials in the whole gland and peripheral zone. Additional performance metrics [Dice similarity coefficient (DSC), mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD)] were also calculated by comparing the MR and TRUS manually segmented prostate surfaces in the registered images. Experimental results showed that the proposed method yielded an overall median TRE of 1.76 mm. The results obtained in terms of DSC showed an average of 80.8±7.8% for the apex of the prostate, 92.0±3.4% for the mid-gland, 81.7±6.4% for the base and 85.7±4.7% for the whole gland. The surface distance calculations showed an overall average of 1.84±0.52 mm for MAD and 6.90±2.07 mm for MAXD.

This study seeks to explore whether chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) can detect liver composition changes between after-meal and over-night-fast statuses.Fifteen healthy volunteers were scanned on a 3.0-T human MRI scanner in the evening 1.5-2 h after dinner and in the morning after over-night (12-h) fasting. Among them, seven volunteers were scanned twice to assess the scan-rescan reproducibility. Images were acquired at offsets (n = 41, increment = 0.25 ppm) from -5 to 5 ppm using a turbo spin echo (TSE) sequence with a continuous rectangular saturation pulse. Amide proton transfer-weighted (APTw) and GlycoCEST signals were quantified with the asymmetric magnetization transfer ratio (MTRasym) at 3.5 ppm and the total MTRasym integrated from 0.5 to 1.5 ppm from the corrected Z-spectrum, respectively. To explore scan time reduction, CEST images were reconstructed using 31 offsets (with 20% time reduction) and 21 offsets (with 40% time reduction), respectively.For reproducibility, GlycoCEST measurements in 41 offsets showed the smallest scan-rescan mean measurements variability, indicated by the lowest mean difference of -0.049% (95% limits of agreement, -0.209 to 0.111%); for APTw, the smallest mean difference was found to be 0.112% (95% limits of agreement, -0.698 to 0.921%) in 41 offsets. Compared with after-meal, both GlycoCEST measurement and APTw measurement under different offset number decreased after 12-h fasting. However, as the offsets number decreased (41 offsets vs. 31 offsets vs. 21 offsets), GlycoCEST map and APTw map became more heterogeneous and noisier.Our results show that CEST liver imaging at 3.0 T has high sensitivity for fasting.

Pulmonary imaging using hyperpolarized 3He/129Xe gas is emerging as a new way to understand the regional nature of pulmonary ventilation abnormalities in obstructive lung diseases. However, the quantitative information derived is completely dependent on robust methods to segment both functional and structural/anatomical data. Here, we propose an approach to jointly segment the lung cavity from 1H and 3He pulmonary magnetic resonance images (MRI) by constraining the spatial consistency of the two segmentation regions, which simultaneously employs the image features from both modalities. We formulated the proposed co-segmentation problem as a coupled continuous min-cut model and showed that this combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In particular, we introduced a dual coupled continuous max-flow model to study the convex relaxed coupled continuous min-cut model under a primal and dual perspective. This gave rise to an efficient duality-based convex optimization algorithm. We implemented the proposed algorithm in parallel using general-purpose programming on graphics processing unit (GPGPU), which substantially increased its computational efficiency. Our experiments explored a clinical dataset of 25 subjects with chronic obstructive pulmonary disease (COPD) across a wide range of disease severity. The results showed that the proposed co-segmentation approach yielded superior performance compared to single-channel image segmentation in terms of precision, accuracy and robustness.

The purpose of this study is to statistically assess whether bi-exponential intravoxel incoherent motion (IVIM) model better characterizes diffusion weighted imaging (DWI) signal of malignant breast tumor than mono-exponential Gaussian diffusion model.3 T DWI data of 29 malignant breast tumors were retrospectively included. Linear least-square mono-exponential fitting and segmented least-square bi-exponential fitting were used for apparent diffusion coefficient (ADC) and IVIM parameter quantification, respectively. F-test and Akaike Information Criterion (AIC) were used to statistically assess the preference of mono-exponential and bi-exponential model using region-of-interests (ROI)-averaged and voxel-wise analysis.For ROI-averaged analysis, 15 tumors were significantly better fitted by bi-exponential function and 14 tumors exhibited mono-exponential behavior. The calculated ADC, D (true diffusion coefficient) and f (pseudo-diffusion fraction) showed no significant differences between mono-exponential and bi-exponential preferable tumors. Voxel-wise analysis revealed that 27 tumors contained more voxels exhibiting mono-exponential DWI decay while only 2 tumors presented more bi-exponential decay voxels. ADC was consistently and significantly larger than D for both ROI-averaged and voxel-wise analysis.Although the presence of IVIM effect in malignant breast tumors could be suggested, statistical assessment shows that bi-exponential fitting does not necessarily better represent the DWI signal decay in breast cancer under clinically typical acquisition protocol and signal-to-noise ratio (SNR). Our study indicates the importance to statistically examine the breast cancer DWI signal characteristics in practice.

Magnetic Resonance Fingerprinting (MRF) is a novel technique that simultaneously estimates multiple tissue-related parameters, such as the longitudinal relaxation time T1, the transverse relaxation time T2, off resonance frequency B0 and proton density, from a scanned object in just tens of seconds. However, the MRF method suffers from aliasing artifacts because it significantly undersamples the k-space data. In this work, we propose a compressed sensing (CS) framework for simultaneously estimating multiple tissue-related parameters based on the MRF method. It is more robust to low sampling ratio and is therefore more efficient in estimating MR parameters for all voxels of an object. Furthermore, the MRF method requires identifying the nearest atoms of the query fingerprints from the MR-signal-evolution dictionary with the L2 distance. However, we observed that the L2 distance is not always a proper metric to measure the similarities between MR Fingerprints. Adaptively learning a distance metric from the undersampled training data can significantly improve the matching accuracy of the query fingerprints. Numerical results on extensive simulated cases show that our method substantially outperforms state-of-the-art methods in terms of accuracy of parameter estimation.

The incorporation of intensity, spatial, and topological information into large-scale multi-region segmentation has been a topic of ongoing research in medical image analysis. Multi-region segmentation problems, such as segmentation of brain structures, pose unique challenges in image segmentation in which regions may not have a defined intensity, spatial, or topological distinction, but rely on a combination of the three. We propose a novel framework within the Advanced segmentation tools (ASETS)(2), which combines large-scale Gaussian mixture models trained via Kohonen self-organizing maps, with deformable registration, and a convex max-flow optimization algorithm incorporating region topology as a hierarchy or tree. Our framework is validated on two publicly available neuroimaging datasets, the OASIS and MRBrainS13 databases, against the more conventional Potts model, achieving more accurate segmentations. Each component is accelerated using general-purpose programming on graphics processing Units to ensure computational feasibility.

OBJECTIVE It was reported lately that to obtain consistent liver T1rho measurement, at 3T MRI using six spin-lock times (SLTs), is feasible. In this study, the feasibility of using three or two SLT points to measure liver T1rho relaxation time was explored. MATERIALS AND METHODS Seventeen healthy volunteers underwent 36 examinations. Three representative axial slices were selected to cut through the upper, middle, and lower liver. A rotary echo spin-lock pulse was implemented in a 2D fast field echo sequence. Spin-lock frequency was 500 Hz and the spin-lock times of 1, 10, 20, 30, 40, and 50 milliseconds (ms) were used for T1rho mapping. T1rho maps were constructed by using all 6 SLT points, three SLT points of 1, 20, and 50 ms, or two SLTs of 1 and 50 ms, respectively. Intra-class correlation coefficient (ICC) and Bland and Altman plot were used to assess the measurement agreement. RESULTS Two examinations were excluded, due to motion artifact at the SLT of 50 ms. With the remaining 34 examinations, the ICC for 6-SLT vs. 3-SLT T1rho measurements was 0.922, while the ICC for 6-SLT vs. 2-SLT T1rho measurement was 0.756. The Bland and Altman analysis showed a mean difference of 0.19 (95% limits of agreement: -1.34, 1.73) for 6-SLT vs. 3-SLT T1rho measurement, and the mean difference of 0.89 (95% limits of agreement: -1.67, 3.45) for 6-SLT vs. 2-SLT T1rho measurement. The scan re-scan reproducibility ICC (n = 11 subjects) was 0.755, 0.727, and 0.528 for 6-SLT measurement, 3-SLT measurement, and 2-SLT measurement, respectively. CONCLUSION Adopting 3 SLTs of 1, 20, and 50 ms can be an acceptable alternative for the liver T1rho measurement, while 2 SLTs of 1 and 50 ms do not provide reliable measurement.

T1ρ relaxation has recently been found to be sensitive to liver fibrosis and has potential to be used for early detection of liver fibrosis and grading. Liver T1ρ imaging and accurate mapping are challenging because of the long scan time, respiration motion and high specific absorption rate. Reduction and optimization of spin lock times (TSLs) are an efficient way to reduce scan time and radiofrequency energy deposition of T1ρ imaging, but maintain the near-optimal precision of T1ρ mapping. This work analyzes the precision in T1ρ estimation with limited, in particular two, spin lock times, and explores the feasibility of using two specific operator-selected TSLs for efficient and accurate liver T1ρ mapping. Two optimized TSLs were derived by theoretical analysis and numerical simulations first, and tested experimentally by in vivo rat liver T1ρ imaging at 3 T. The simulation showed that the TSLs of 1 and 50 ms gave optimal T1ρ estimation in a range of 10–100 ms. In the experiment, no significant statistical difference was found between the T1ρ maps generated using the optimized two-TSL combination and the maps generated using the six TSLs of [1, 10, 20, 30, 40, 50] ms according to one-way ANOVA analysis (p = 0.1364 for liver and p = 0.8708 for muscle).

The capability to image temperature is a very attractive feature of MRI and has been actively exploited for guiding minimally-invasive thermal therapies. Among many MR-based temperature-sensitive approaches, proton resonance frequency (PRF) thermometry provides the advantage of excellent linearity of signal with temperature over a large temperature range. Furthermore, the PRF shift has been shown to be fairly independent of tissue type and thermal history. For these reasons, PRF method has evolved into the most widely used MR-based thermometry method. In the present paper, the basic principles of PRF-based temperature mapping will be reviewed, along with associated pulse sequence designs. Technical advancements aimed at increasing the imaging speed and/or temperature accuracy of PRF-based thermometry sequences, such as image acceleration, fat suppression, reduced field-of-view imaging, as well as motion tracking and correction, will be discussed. The development of accurate MR thermometry methods applicable to moving organs with non-negligible fat content represents a very challenging goal, but recent developments suggest that this goal may be achieved. If so, MR-guided thermal therapies may be expected to play an increasingly-important therapeutic and palliative role, as a minimally-invasive alternative to surgery.

This paper studies continuous image labeling problems with an arbitrary data term and a total variation regularizer, where the labels are constrained to a finite set of real numbers. Inspired by Ishikawa’s multi-layered graph construction for the same labeling problem over a discrete image domain, we propose a novel continuous max-flow model and build up its duality to a convex relaxed formulation of image labeling under a new variational perspective. Via such continuous max-flow formulations, we show that exact and global optimizers can be obtained to the original non-convex labeling problem. We also extend the studies to problems with continuous-valued labels and introduce a new theory to this problem. Finally, we show the proposed continuous max-flow models directly lead to new fast flow-maximization algorithmic schemes which outperform previous approaches in terms of efficiency. Such continuous max-flow based algorithms can be validated by convex optimization theories and accelerated by modern parallel computational hardware.

A three-dimensional (3-D) ultrasound (US) system has been developed to monitor the intracranial ventricular system of preterm neonates with intraventricular hemorrhage (IVH) and the resultant dilation of the ventricles (ventriculomegaly). To measure ventricular volume from 3-D US images, a semi-automatic convex optimization-based approach is proposed for segmentation of the cerebral ventricular system in preterm neonates with IVH from 3-D US images. The proposed semi-automatic segmentation method makes use of the convex optimization technique supervised by user-initialized information. Experiments using 58 patient 3-D US images reveal that our proposed approach yielded a mean Dice similarity coefficient of 78.2% compared with the surfaces that were manually contoured, suggesting good agreement between these two segmentations. Additional metrics, the mean absolute distance of 0.65 mm and the maximum absolute distance of 3.2 mm, indicated small distance errors for a voxel spacing of 0.22 × 0.22 × 0.22 mm(3). The Pearson correlation coefficient (r = 0.97, p < 0.001) indicated a significant correlation of algorithm-generated ventricular system volume (VSV) with the manually generated VSV. The calculated minimal detectable difference in ventricular volume change indicated that the proposed segmentation approach with 3-D US images is capable of detecting a VSV difference of 6.5 cm(3) with 95% confidence, suggesting that this approach might be used for monitoring IVH patients' ventricular changes using 3-D US imaging. The mean segmentation times of the graphics processing unit (GPU)- and central processing unit-implemented algorithms were 50 ± 2 and 205 ± 5 s for one 3-D US image, respectively, in addition to 120 ± 10 s for initialization, less than the approximately 35 min required by manual segmentation. In addition, repeatability experiments indicated that the intra-observer variability ranges from 6.5% to 7.5%, and the inter-observer variability is 8.5% in terms of the coefficient of variation of the Dice similarity coefficient. The intra-class correlation coefficient for ventricular system volume measurements for each independent observer ranged from 0.988 to 0.996 and was 0.945 for three different observers. The coefficient of variation and intra-class correlation coefficient revealed that the intra- and inter-observer variability of the proposed approach introduced by the user initialization was small, indicating good reproducibility, independent of different users.

Purpose To evaluate the feasibility of T 1 rho mapping in myocardium at 3T and to determine whether T 1 rho mapping could better characterize myocardial injury in end‐stage renal disease (ESRD) patients compared to T 1 and T 2 mapping. Materials and Methods T 1 rho mapping, T 1 mapping, and T 2 mapping were performed at 3T on 35 healthy volunteers (15 males, 20 females, 40.7 ± 13.6 years) and 32 ESRD patients (16 males, 16 females, 48.6 ± 11.9 years). The mean T 1 rho, T 1 , and T 2 values were compared using Student's t ‐test and correlated with cardiac function parameters, including peak ejection rate (PER), short‐axis percent thickening (SAPT), peak filling rate (PFR), and time to peak filling (TTPF). Results The mean T 1 rho values (49.4 ± 2.6 msec vs. 52.2 ± 4.0 msec, P = 0.001) and T 2 values (50.5 ± 2.5 msec vs. 54.1 ± 4.0 msec, P &lt; 0.001) were significantly different between the volunteers and patients, but there were no significant differences between the two groups in the T 1 values (1253.1 ± 71.6 msec vs. 1273.4 ± 41.7 msec, P = 0.157). The mean T 1 rho values were negatively correlated with the PER ( r = –0.397; P = 0.03), SAPT ( r = –0.688; P &lt; 0.001), and PFR ( r = –0.537; P = 0.002), whereas positively correlated with the TTPF ( r = 0.677; P &lt; 0.001). The mean T 2 values were negatively correlated only with the SAPT ( r = –0.427; P = 0.019) in the ESRD patients. Conclusion T 1 rho mapping of the myocardium is feasible at 3T. T 1 rho values are higher in ESRD patients and relate to cardiac function, which may better characterize myocardial injury than can T 1 and T 2 . J. Magn. Reson. Imaging 2016;44:723–731.

To evaluate Chemical Exchange Saturation Transfer (CEST) MRI for liver imaging at 3.0-T.Images were acquired at offsets (n = 41, increment = 0.25 ppm) from -5 to 5 ppm using a TSE sequence with a continuous rectangular saturation pulse. Amide proton transfer-weighted (APTw) and GlycoCEST signals were quantified as the asymmetric magnetization transfer ratio (MTRasym) at 3.5 ppm and the total MTRasym integrated from 0.5 to 1.5 ppm, respectively, from the corrected Z-spectrum. Reproducibility was assessed for rats and humans. Eight rats were devoid of chow for 24 hours and scanned before and after fasting. Eleven rats were scanned before and after one-time CCl4 intoxication.For reproducibility, rat liver APTw and GlycoCEST measurements had 95 % limits of agreement of -1.49 % to 1.28 % and -0.317 % to 0.345 %. Human liver APTw and GlycoCEST measurements had 95 % limits of agreement of -0.842 % to 0.899 % and -0.344 % to 0.164 %. After 24 hours, fasting rat liver APTw and GlycoCEST signals decreased from 2.38 ± 0.86 % to 0.67 ± 1.12 % and from 0.34 ± 0.26 % to -0.18 ± 0.37 % respectively (p < 0.05). After CCl4 intoxication rat liver APTw and GlycoCEST signals decreased from 2.46 ± 0.48 % to 1.10 ± 0.77 %, and from 0.34 ± 0.23 % to -0.16 ± 0.51 % respectively (p < 0.05).CEST liver imaging at 3.0-T showed high sensitivity for fasting as well as CCl4 intoxication.• CEST MRI of in-vivo liver was demonstrated at clinical 3 T field strength. • After 24-hour fasting, rat liver APTw and GlycoCEST signals decreased significantly. • After CCl4 intoxication both rat liver APTw and GlycoCEST signals decreased significantly. • Good scan-rescan reproducibility of liver CEST MRI was shown in healthy volunteers.

The recovery of nitrogen (N) and phosphorus (P) from wastewater is of great importance in addressing the global nutrient crisis. The limitations of existing methods require the development of effective technology. Here, two different hydrogel adsorbents were fabricated with good separation ability for metal cation (M+) and metal anion (M−) but showed little removal of nutrients. Based on the materials, a novel three-stage operation system combining adsorption and capacitive deionization (CDI) technology was presented for nutrient recovery and wastewater treatment. In the first two stages, mixed metals in wastewater were successfully separated (Cu2+: 144.6 mg/g; Cr2O72−: 167.0 mg/g), and nutrients were retained (N and P < 1 mg/g). In the third stage, the residual trace metal ions in the solution were removed (2.0 mg/L to N/A), and the nutrients were enriched through electroadsorption and desorption processes by CDI. Plants using recovered liquid fertilizers revealed similar values for height, root length, and chlorophyll compared with those obtained using actual fertilizers. The results indicated that this novel three-stage operation system (3S A–C system) combining adsorption and CDI is efficient in recovering liquid fertilizers from wastewater and is a promising technology for simultaneously addressing nutrient crises and environmental pollution.

Purpose To evaluate whether pre-treatment intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) can predict treatment outcome after 2 years in patients with nasopharyngeal carcinoma (NPC). Method One hundred and sixty-one patients with newly diagnosed NPC underwent pre-treatment IVIM-DWI. Univariate Cox regression analysis was performed to evaluate the correlation of the mean values of the pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction and apparent diffusion coefficient with local relapse-free survival (LRFS), regional relapse-free survival (RRFS), distant metastases-free survival (DMFS) and disease-free survival (DFS). Significant diffusion parameters, together with staging, age, gender and treatment as confounding factors, were added into a multivariate model. The area under the curves (AUCs) of significant parameters for disease relapse were compared using the Delong test. Results Disease relapse occurred in 30 % of the patients at a median follow-up time of 52.1 months. The multivariate analysis showed that high D and T-staging were correlated with poor LRFS (p = 0.042 and 0.020, respectively) and poor DFS (p = 0.023 and 0.001, respectively); low D* and high T-staging with poor RRFS (p = 0.020 and 0.033, respectively); and high N-staging with poor DMFS (p = 0.006). D with the optimal threshold of ≥0.68 × 10−3 mm2/s and T-staging showed similar AUCs (AUC = 0.614 and 0.651, respectively; p = 0.493) for predicting disease relapse. Conclusion High D and low D* were predictors of poor locoregional outcome but none of the diffusion parameters predicted DMFS in NPC.

MRI pulse sequences and imaging parameters substantially influence the variation of MRI radiomics features, thus impose a critical challenge on MRI radiomics reproducibility and reliability. This study aims to prospectively investigate the impact of various imaging parameters on MRI radiomics features in a 3D T2-weighted (T2W) turbo-spin-echo (TSE) pulse sequence for MR-guided-radiotherapy (MRgRT).An anthropomorphic phantom was scanned using a 3D-T2W-TSE MRgRT sequence at 1.5T under a variety of acquisition imaging parameter changes. T1 and T2 relaxation times of the phantom were also measured. 93 first-order and texture radiomics features in the original and 14 transformed images, yielding 1,395 features in total, were extracted from 10 volumes-of-interest (VOIs). The percentage deviation (d%) of radiomics feature values from the baseline values and intra-class correlation coefficient (ICC) with the baseline were calculated. Robust radiomics features were identified based on the excellent agreement of radiomics feature values with the baseline, i.e., the averaged d% <5% and ICC >0.90 in all VOIs for all imaging parameter variations.The radiomics feature values changed considerably but to different degrees with different imaging parameter adjustments, in the ten VOIs. The deviation d% ranged from 0.02% to 321.3%, with a mean of 12.5% averaged for all original features in all ten VOIs. First-order and GLCM features were generally more robust to imaging parameters than other features in the original images. There were also significantly different radiomics feature values (ANOVA, P<0.001) between the original and the transformed images, exhibiting quite different robustness to imaging parameters. 330 out of 1395 features (23.7%) robust to imaging parameters were identified. GLCM and GLSZM features had the most (42.5%, 153/360) and least (3.8%, 9/240) robust features in the original and transformed images, respectively.This study helps better understand the quantitative dependence of radiomics feature values on imaging parameters in a 3D-T2W-TSE sequence for MRgRT. Imaging parameter heterogeneity should be considered as a significant source of radiomics variability and uncertainty, which must be well harmonized for reliable clinical use. The identified robust features to imaging parameters are helpful for the pre-selection of radiomics features for reliable radiomics modeling.

Purpose The purpose of this study was to quantitatively assess the longitudinal acquisition repeatability of MRI radiomics features in a three‐dimensional (3D) T1‐weighted (T1W) TSE sequence via a well‐controlled prospective phantom study. Methods Thirty consecutive daily datasets of an ACR‐MRI phantom were acquired on two 1.5T MRI simulators using a 3D T1W TSE sequence. Images were blindly segmented by two observers. Post‐acquisition processing was minimized but an intensity discretization (fixed bin size of 25). One hundred and one radiomics features (shape n = 12; first order n = 16; texture n = 73) were extracted. Longitudinal repeatability of each feature was evaluated by Pearson correlation and coefficient of variance (CV 68% ). Interobserver feature value agreement was also quantified using intraclass correlation coefficient (ICC) and Bland–Altman analysis. A most repeatable radiomics feature set on both scanners was determined by feature coefficient of variance (CV 68% &lt;5%), ICC (&gt;0.75), and the ratio of the interobserver difference to the interobserver mean δ&lt;5%. Results No trend of radiomics feature value changed with time. Longitudinal feature repeatability CV 68% ranged 0.01–38.60% (mean/median: 12.5%/9.9%), and 0.01–40.47%, (8.49%/7.34%) on the scanners A and B. Shape features exhibited significantly better repeatability than first‐order and texture features (all P &lt; 0.01). Significant longitudinal repeatability difference was observed in texture features ( P &lt; 0.001) between the two scanners, but not in shape and first‐order features ( P &gt; 0.30). First‐order and texture features had smaller interobserver‐dependent variation than acquisition‐dependent variation. They also showed good interobserver agreement on both scanners (A:ICC = 0.80 ± 0.23; B:ICC = 0.80 ± 0.22), independent of acquisition repeatability. The repeatable radiomics features in common on both scanners, including 12 shape features, 0 first‐order features, and 3 texture features, were determined as the most repeatable MRI radiomics feature set. Conclusions Radiomics features exhibited heterogeneous longitudinal repeatability, while the shape features were the most repeatable, in this phantom study with a 3D T1W TSE acquisition. The most repeatable radiomics feature set derived in this study should be helpful for the selection of reliable radiomics features in the future clinical use.

It is challenging to deploy check-in nodes optimally in a complex network so as to perform specific check-in like services, e.g., fuel supplements etc., but crucial in many real-world applications. In this article, we propose and study the new optimization problem of placing check-in nodes with the minimum cost, i.e., the problem of finding the minimum-cost check-in nodes (MCCN), which is of great interests for real-world application situations, but even more difficult. We motivate the new algorithms through three typical worse cases by the often-used greedy-type algorithms, i.e., One-to-Many, Many-to-One, and Duplicate-Overrides. With this respect, the proposed novel optimization algorithms utilize a novel metric of contribution density for selecting check-in nodes iteratively, which successfully avoid the occurrence of the two worse cases of One-to-Many and Many-to-One. We also introduce an extra backward extraction step in one of new algorithms, which overcomes the crucial worse case of “duplicate overrides” and largely improves the algorithmic performance in solving the introduced optimization problem of MCCN. Meanwhile, we extend the new contribution density metric to a more general class of functions and study their effectiveness to eliminate the two worse cases of One-to-Many and Many-to-One; also, a detailed analysis on complexity and performance of the proposed algorithms is presented to show their numerical efficiency and accuracy. Extensive experiments over two classical artificial networks, i.e., BA network and ER network, and ten real-world networks, under two typical cost setups, show the proposed algorithms significantly outperform the four state-of-the-art algorithms. We also demonstrate that our proposed algorithms are much reliable and robust with different experiment settings of deployment cost and network-type and scale.

Social deficits are among the most important functional impairments in attention-deficit/hyperactivity disorder (ADHD). However, the relationship between social impairment and ADHD core symptoms as well as the underlying cerebral blood flow (CBF) characteristics remain unclear.A total of 62 ADHD subjects with social deficits (ADHD + SD), 100 ADHD subjects without social deficits (ADHD-SD) and 81 age-matched typically developing controls (TDC) were enrolled. We first examined the correlation between the Social Responsiveness Scale (SRS-1) and ADHD core symptoms (inattention, hyperactivity, and impulsion) and then explored categorical and dimensional ADHD-related regional CBF by arterial spin labeling (ASL). For the categorical analysis, a voxel-based comparison of CBF maps between the ADHD + SD, ADHD-SD, and TDC groups was performed. For the dimensional analysis, the whole-brain voxel-wise correlation between CBF and ADHD symptoms (inattention, hyperactivity/impulsivity, and total scores) was evaluated in three groups. Finally, correlations between the SRS-1 and ADHD-related regional CBF were investigated. We applied Gaussian random field (GRF) for the correction of multiple comparisons in imaging results (voxel-level P < 0.01, and cluster-level P < 0.05).The clinical characteristics analysis showed that social deficits positively correlated with ADHD core symptoms, especially in social communication and autistic mannerisms domains. In the categorical analysis, we found that CBF in the left middle/inferior temporal gyrus in ADHD groups was higher than TDCs and was negatively correlated with the social motivation scores. Moreover, in dimensional analysis, we found that CBF in the left middle frontal gyrus was negatively correlated with the inattention scores, SRS total scores and autistic mannerisms scores in ADHD + SD subjects.The present study shows that inattention, hyperactivity, and impulsivity may be responsible for the occurrence of social deficits in ADHD, with autistic traits being another significant contributing factor. Additionally, CBF in the left middle/inferior temporal gyrus and the left middle frontal gyrus might represent the corresponding physiological mechanisms underlying social deficits in ADHD.

Abstract Interconnecting L/C components are often applied to decouple the array elements for parallel imaging. Although it has been recognized that interconnecting capacitors and inductors can both be employed for decoupling, quantitative study of this decoupling technique has not yet been presented. In this study, a theoretical analysis for the interconnecting L/C decoupling circuit is provided. The analysis reveals that the required decoupling capacitance decreases with the resonant frequency, whereas the decoupling inductance is independent of the frequency. The inductive decoupling scheme is applied in the design of a four‐channel knee coil for 0.5 T open MRI with vertical magnetic field. Experimental results show that good isolations (−19 dB ∼ −45 dB) between coil elements can be achieved and only 5% ∼ 11% degeneration of Q is caused by this decoupling method. © 2007 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 31B: 116–126, 2007

Abstract MR‐based thermometry is a valuable adjunct to thermal ablation therapies as it helps to determine when lethal doses are reached at the target and whether surrounding tissues are safe from damage. When the targeted lesion is mobile, MR data can further be used for motion‐tracking purposes. The present work introduces pulse sequence modifications that enable significant improvements in terms of both temperature‐to‐noise‐ratio properties and target‐tracking abilities. Instead of sampling a single magnetization pathway as in typical MR thermometry sequences, the pulse‐sequence design introduced here involves sampling at least one additional pathway. Image reconstruction changes associated with the proposed sampling scheme are also described. The method was implemented on two commonly used MR thermometry sequences: the gradient‐echo and the interleaved echo‐planar imaging sequences. Data from the extra pathway enabled temperature‐to‐noise‐ratio improvements by up to 35%, without increasing scan time. Potentially of greater significance is that the sampled pathways featured very different contrast for blood vessels, facilitating their detection and use as internal landmarks for tracking purposes. Through improved temperature‐to‐noise‐ratio and lesion‐tracking abilities, the proposed pulse‐sequence design may facilitate the use of MR‐monitored thermal ablations as an effective treatment option even in mobile organs such as the liver and kidneys. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.

The chemical exchange (CE) process has been exploited as a novel and powerful contrast mechanism for MRI, which is primarily performed in the form of chemical exchange saturation transfer (CEST) imaging. A spin-lock (SL) technique can also be used for CE studies, although traditionally performed and interpreted quite differently from CEST. Chemical exchange imaging with spin-lock technique (CESL), theoretically based on the Bloch-McConnell equations common to CEST, has the potential to be used as an alternative to CEST and to better characterize CE processes from slow and intermediate to fast proton exchange rates through the tuning of spin-lock pulse parameters. In this study, the Z-spectrum and asymmetric magnetization transfer ratio (MTR(asym)) obtained by CESL are theoretically analyzed and numerically simulated using a general two-pool R(1ρ) relaxation model beyond the fast-exchange limit. The influences of spin-lock parameters, static magnetic field strength B(0) and physiological properties on the Z-spectrum and MTR(asym) are quantitatively revealed. Optimization of spin-lock frequency and spin-lock duration for the maximum CESL contrast enhancement is also investigated. Numerical simulation results in this study are compatible with the findings in the existing literature on CE imaging studies.

We present a novel and fast interactive approach to multi-modality cardiac image segmentation, which employs the linearly ordered surfaces as an additional constraint. We show using such a geometrical constraint helps to significantly reduce user interaction and improve the accuracy of segmentation results at the same time. We solve the proposed multiregion segmentation problem with the order constraints by means of convex optimization, resulting in a fast and reliable flow maximization approach which implicitly embeds the linear order prior without introducing extra computation load. In this regard, a new fully parallelized continuous max-flow algorithm is proposed and implemented using GPGPU to segment a 3D volume within one second. We demonstrate our results over pathological trans-esophageal echocardiogram, cardiac CT and delayed enhancement MRI data sets.

Purpose: Prostate segmentation is an important step in the planning and treatment of 3D end‐firing transrectal ultrasound (TRUS) guided prostate biopsy. In order to improve the accuracy and efficiency of prostate segmentation in 3D TRUS images, an improved level set method is incorporated into a rotational‐slice‐based 3D prostate segmentation to decrease the accumulated segmentation errors produced by the slice‐by‐slice segmentation method. Methods: A 3D image is first resliced into 2D slices in a rotational manner in both the clockwise and counterclockwise directions. All slices intersect approximately along the rotational scanning axis and have an equal angular spacing. Six to eight boundary points are selected to initialize a level set function to extract the prostate contour within the first slice. The segmented contour is then propagated to the adjacent slice and is used as the initial contour for segmentation. This process is repeated until all slices are segmented. A modified distance regularization level set method is used to segment the prostate in all resliced 2D slices. In addition, shape‐constraint and local‐region‐based energies are imposed to discourage the evolved level set function to leak in regions with weak edges or without edges. An anchor point based energy is used to promote the level set function to pass through the initial selected boundary points. The algorithm's performance was evaluated using distance‐ and volume‐based metrics (sensitivity (Se), Dice similarity coefficient (DSC), mean absolute surface distance (MAD), maximum absolute surface distance (MAXD), and volume difference) by comparison with expert delineations. Results: The validation results using thirty 3D patient images showed that the authors’ method can obtain a DSC of 93.1% ± 1.6%, a sensitivity of 93.0% ± 2.0%, a MAD of 1.18 ± 0.36 mm, a MAXD of 3.44 ± 0.8 mm, and a volume difference of 2.6 ± 1.9 cm 3 for the entire prostate. A reproducibility experiment demonstrated that the proposed method yielded low intraobserver and interobserver variability in terms of DSC. The mean segmentation time of the authors’ method for all patient 3D TRUS images was 55 ± 3.5 s, in addition to 30 ± 5 s for initialization. Conclusions: To address the challenges involved with slice‐based 3D prostate segmentation, a level set based method is proposed in this paper. This method is especially developed for a 3D end‐firing TRUS guided prostate biopsy system. The extensive experimental results demonstrate that the proposed method is accurate, robust, and computationally efficient.

In this study, we propose an efficient non-rigid MR-TRUS deformable registration method to improve the accuracy of targeting suspicious locations during a 3D ultrasound (US) guided prostate biopsy. The proposed deformable registration approach employs the multi-channel modality independent neighbourhood descriptor (MIND) as the local similarity feature across the two modalities of MR and TRUS, and a novel and efficient duality-based convex optimization based algorithmic scheme is introduced to extract the deformations which align the two MIND descriptors. The registration accuracy was evaluated using 10 patient images by measuring the TRE of manually identified corresponding intrinsic fiducials in the whole gland and peripheral zone, and performance metrics (DSC, MAD and MAXD) for the apex, mid-gland and base of the prostate were also calculated by comparing two manually segmented prostate surfaces in the registered 3D MR and TRUS images. Experimental results show that the proposed method yielded an overall mean TRE of 1.74 mm, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm.

This study investigates a convex relaxation approach to figure-ground separation with a global distribution matching prior evaluated by the Bhattacharyya measure. The problem amounts to finding a region that most closely matches a known model distribution. It has been previously addressed by curve evolution, which leads to suboptimal and computationally intensive algorithms, or by graph cuts, which result in metrication errors. Solving a sequence of convex subproblems, the proposed relaxation is based on a novel bound of the Bhattacharyya measure which yields an algorithm robust to initial conditions. Furthermore, we propose a novel flow configuration that accounts for labeling-function variations, unlike existing configurations. This leads to a new max-flow formulation which is dual to the convex relaxed subproblems we obtained. We further prove that such a formulation yields exact and global solutions to the original, nonconvex subproblems. A comprehensive experimental evaluation on the Microsoft GrabCut database demonstrates that our approach yields improvements in optimality and accuracy over related recent methods.

This report aims to study the age related T1rho and T2 relaxation time changes in lumbar intervertebral disc. Lumbar sagittal magnetic resonance imaging (MRI) was performed with a 3 Tesla scanner in 52 subjects. With a spin-lock frequency of 500 Hz, T1rho was measured using a rotary echo spin-lock pulse embedded in a 3D balanced fast field echo sequence. A multi-echo turbo spin echo sequence was used for T2 mapping. Regions-of-interest were drawn over the T1rho and T2 maps, including nucleus pulposus and annulus fibrosus. For L1/2-L4/5 discs, results showed the age associated reduction of T1rho of nucleus pulposus had a of slope of -1.06, the reduction of T2 of nucleus pulposus had a slope of -1.47, the reduction of T1rho of annulus fibrosus had a slope of -0.25, and the reduction of T2 of annulus fibrosus had a slope of -0.18, with all the slopes significantly non-zero. In nucleus pulposus the slope of T2 was slightly steeper than that of T1rho (P=0.085), while in annulus fibrosus the slope of T1rho was slightly steeper than that of T2 (P=0.31). We conclude that significant age related reduction of T1rho and T2 magnetic resonance relaxation times of lumbar intervertebral disc was observed, however, the relative performances of T1rho vs. T2 were broadly similar.

Chronic liver disease is a major public health problem worldwide. Liver fibrosis, a common feature of almost all chronic liver diseases, involves the accumulation of collagen, proteoglycans, and other macromolecules in the extracellular matrix. The accumulation of proteins in the extracellular matrix promotes the formation of scars that bridge together adjacent portal triads and central veins. Ultimately, progressive hepatic fibrosis leads to cirrhosis, a characteristic of all end-stage liver disease. Clinically, liver fibrosis usually has an insidious onset and progresses slowly over decades. Patients remain asymptomatic or have only mild, nonspecific symptoms until the development of cirrhosis (1). To date, the conventional imaging diagnostic tests available in clinical practice are not sensitive or specific enough to function as screening tests for detecting liver fibrosis. In patients with precirrhotic stages of liver fibrosis, as well as patients with early cirrhosis, the liver parenchyma usually has a normal appearance or may exhibit only subtle, nonspecific heterogeneity on conventional MR images. A number of MR imaging techniques have been investigated to identify or to assign a grade to liver cirrhosis. These include double contrast material-enhanced MR imaging (2), MR elastography (3-5), and diffusion weighted imaging (3,5,6). The reproducibility and intersite variability of these techniques have not been well established. A mechanism for magnetic resonance (MR) tissue contrast, spin-lattice relaxation time in the rotating frame (T1ρ), has been investigated in biomedical applications. In T1ρ imaging, the equilibrium magnetization, M0, established by the static magnetic field, B0, is flipped into the transverse plane first. This magnetization in the transverse plane relaxes like a normal free induction decay but in the presence of an onresonant continuous wave radiofrequency pulse, which is much weaker than B0 and is called spin-lock radiofrequency pulse. The relaxation rate constant of this transverse magnetization