Multi-contrast Magnetic Resonance Imaging Research Articles

Predictors of Ipsilateral New Ischemic Lesions on Diffusion-Weighted Imaging after Carotid Artery Stenting in Asymptomatic Patients: A Retrospective Observational Study with Conventional Multicontrast MRI

The purpose of this study was to identify the independent risk factors for ipsilateral new ischemic lesions (NILs) during carotid artery stenting (CAS). In patients treated with CAS, the association between postoperative ipsilateral NILs on diffusion-weighted imaging (DWI) and patient demographics, intraoperative factors, the presence of plaque components, the semiquantitative grading of component size on multicontrast magnetic resonance imaging (MRI) were retrospectively analyzed. Ipsilateral NILs on DWI were detected in 85 (39.2%) patients. The debris was observed on the surface embolic protection devices in 70.97% of patients. Univariate analysis showed that different stages of intraplaque hemorrhage (IPH) (along with lipid-rich necrotic core [LRNC]) (P < 0.001), size of IPH (P < 0.001), calcification (CA) (P = 0.045), and LRNC (without IPH) (P < 0.001) as well as postdilation (P < 0.001)), stent type (P = 0.001), and aortic arch ulcer (P = 0.004) were associated with postoperative ipsilateral NILs. Multivariate logistic regression analysis showed that the acute and recent IPH (along with LRNC) (odds ratio [OR]: 5.77, P < 0.002 and OR: 28.66, P < 0.001, respectively), LRNC size in Grade 2 (OR: 6.10, P < 0.001) were independent risk factors for ipsilateral NILs. Aortic arch ulcer (OR: 3.44, P = 0.002), postdilation (OR: 4.72, P = 0.04) and open cell stent (OR: 2.88, P < 0.016) were also significantly related to ipsilateral NILs on DWI after CAS. There was a significant correlation between IPH at different stages and their grade of size (correlation coefficient: 0.89; P < 0.001). The IPH and larger LRNC along with the aortic arch ulcer, postdilation and open cell stent are associated with increased risk of ipsilateral NILs on DWI after CAS procedure. Preoperative staging of IPH and semiquantitative grading of size of plaque components based on multi-contrast MRI may be useful for predicting ipsilateral cerebral ischemic events after CAS.

Influence of Pulse Wave Velocity on Atherosclerosis and Blood Flow Reversal in the Aorta: A 4-Dimensional Flow Magnetic Resonance Imaging Study in Acute Stroke Patients and Matched Controls.

Aortic stiffness is associated with a higher incidence of cardiovascular events including stroke. The primary aim of this study was to evaluate whether increased pulse wave velocity (PWV), a marker of stiffness, is an independent predictor of aortic atheroma. The secondary aim was to test whether increased PWV reinforces retrograde blood flow from the descending aorta (DAo), a mechanism of stroke. We performed a cross-sectional case-control study with prospective data acquisition. In all, 40 stroke and 60 ophthalmic patients matched for age and cardiovascular risk factors were included. Multicontrast magnetic resonance imaging (MRI) protocol of the aorta tailored to allow a detailed plaque analysis using 3-dimensional (D) T1-weighted bright blood, T2-weighted and proton density-weighted black blood, and hemodynamic assessment using 4D flow MRI was applied. Individual PWV was calculated based on 4D flow MRI data using the time-to-foot of the blood flow waveform. The extent of maximum retrograde blood flow from the proximal DAo into the arch was quantified. PWV was higher in stroke patients compared with controls (7.62±2.59 vs. 5.96±2.49 m/s; P=0.005) and in patients with plaques (irrespective of thickness) compared with patients without plaques (7.47±2.89 vs. 5.62±1.89 m/s; P=0.002). Increased PWV was an independent predictor of plaque prevalence and contributed significantly to a predictor model explaining 36.5% (Nagelkerke R2) of the variance in plaque presence. Maximum retrograde flow extent from the proximal DAo was not correlated with PWV. Aortic stiffness was higher in stroke patients and associated with a higher prevalence of plaques. Increased PWV was an independent predictor of plaque presence. Accordingly, regional PWV seems to be a valuable biomarker for the assessment and management of aortic atherosclerosis. However, no association was found for increased retrograde flow extent from the DAo.

On the regularization of feature fusion and mapping for fast MR multi-contrast imaging via iterative networks

Multi-contrast (MC) Magnetic Resonance Imaging (MRI) of the same patient usually requires long scanning times, despite the images sharing redundant information. In this work, we propose a new iterative network that utilizes the sharable information among MC images for MRI acceleration. The proposed network has reinforced data fidelity control and anatomy guidance through an iterative optimization procedure of Gradient Descent, leading to reduced uncertainties and improved reconstruction results. Through a convolutional network, the new method incorporates a learnable regularization unit that is capable of extracting, fusing, and mapping shareable information among different contrasts. Specifically, a dilated inception block is proposed to promote multi-scale feature extractions and increase the receptive field diversity for contextual information incorporation. Lastly, an optimal MC information feeding protocol is built through the design of a complementary feature extractor block. Comprehensive experiments demonstrated the superiority of the proposed network, both qualitatively and quantitatively.

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and research investigations in various conditions such as aging, diabetes mellitus, obesity, metabolic syndrome, and their associated comorbidities. Towards a fully automated, robust, and precise quantification of thigh tissues, herein we designed a novel semi-supervised segmentation algorithm based on deep network architectures. Built upon Tiramisu segmentation engine, our proposed deep networks use variational and specially designed targeted dropouts for faster and robust convergence, and utilize multi-contrast MRI scans as input data. In our experiments, we have used 150 scans from 50 distinct subjects from the Baltimore Longitudinal Study of Aging (BLSA). The proposed system made use of both labeled and unlabeled data with high efficacy for training, and outperformed the current state-of-the-art methods with dice scores of 97.52%, 94.61%, 80.14%, 95.93%, and 96.83% for muscle, fat, IMAT, bone, and bone marrow tissues, respectively. Our results indicate that the proposed system can be useful for clinical research studies where volumetric and distributional tissue quantification is pivotal and labeling is a significant issue. To the best of our knowledge, the proposed system is the first attempt at multi-tissue segmentation using a single end-to-end semi-supervised deep learning framework for multi-contrast thigh MRI scans.

Synthesizing Multi-Contrast MR Images Via Novel 3D Conditional Variational Auto-Encoding GAN

As two different modalities of medical images, Magnetic Resonance (MR) and Computer Tomography (CT), provide mutually-complementary information to doctors in clinical applications. However, to obtain both images sometimes is cost-consuming and unavailable, particularly for special populations. For example, patients with metal implants are not suitable for MR scanning. Also, it is probably infeasible to acquire multi-contrast MR images during once clinical scanning. In this context, to synthesize needed MR images for patients whose CT images are available becomes valuable. To this end, we present a novel generative network, called CAE-ACGAN, which incorporates the advantages of Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN) with an auxiliary discriminative classifier network. We apply this network to synthesize multi-contrast MR images from single CT and conduct experiments on brain datasets. Our main contributions can be summarized as follows: 1)We alleviate the problems of images blurriness and mode collapse by integrating the advantages of VAE and GAN; 2) We solve the complicated cross-domain, multi-contrast MR synthesis task using the proposed network; 3) The technique of random-extraction-patches is used to lower the limit of insufficient training data, enabling to obtain promising results even with limited available data; 4) By comparing with other typical networks, we are able to yield nearer-real, higher-quality synthetic MR images, demonstrating the effectiveness and stability of our proposed network.

Synthesize High-Quality Multi-Contrast Magnetic Resonance Imaging From Multi-Echo Acquisition Using Multi-Task Deep Generative Model.

Multi-echo saturation recovery sequence can provide redundant information to synthesize multi-contrast magnetic resonance imaging. Traditional synthesis methods, such as GE's MAGiC platform, employ a model-fitting approach to generate parameter-weighted contrasts. However, models' over-simplification, as well as imperfections in the acquisition, can lead to undesirable reconstruction artifacts, especially in T2-FLAIR contrast. To improve the image quality, in this study, a multi-task deep learning model is developed to synthesize multi-contrast neuroimaging jointly using both signal relaxation relationships and spatial information. Compared with previous deep learning-based synthesis, the correlation between different destination contrast is utilized to enhance reconstruction quality. To improve model generalizability and evaluate clinical significance, the proposed model was trained and tested on a large multi-center dataset, including healthy subjects and patients with pathology. Results from both quantitative comparison and clinical reader study demonstrate that the multi-task formulation leads to more efficient and accurate contrast synthesis than previous methods.

MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice.

Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variability in image analysis techniques can lead to inconsistent results. We have developed quantitative imaging for application in the preclinical arm of a coclinical trial by using a genetically engineered mouse model of soft tissue sarcoma. Magnetic resonance imaging (MRI) images were acquired 1 day before and 1 week after radiation therapy. After the second MRI, the primary tumor was surgically removed by amputating the tumor-bearing hind limb, and mice were followed for up to 6 months. An automatic analysis pipeline was used for multicontrast MRI data using a convolutional neural network for tumor segmentation followed by radiomics analysis. We then calculated radiomics features for the tumor, the peritumoral area, and the 2 combined. The first radiomics analysis focused on features most indicative of radiation therapy effects; the second radiomics analysis looked for features that might predict primary tumor recurrence. The segmentation results indicated that Dice scores were similar when using multicontrast versus single T2-weighted data (0.863 vs 0.861). One week post RT, larger tumor volumes were measured, and radiomics analysis showed greater heterogeneity. In the tumor and peritumoral area, radiomics features were predictive of primary tumor recurrence (AUC: 0.79). We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies.

Reconstruction of multicontrast MR images through deep learning.

Magnetic resonance (MR) imaging with a long scan time can lead to degraded images due to patient motion, patient discomfort, and increased costs. For these reasons, the role of rapid MR imaging is important. In this study, we propose the joint reconstruction of multicontrast brain MR images from down-sampled data to accelerate the data acquisition process using a novel deep-learning network. Twenty-one healthy volunteers (female/male=7/14, age=26±4yr, range 22-35yr) and 16 postoperative patients (female/male=7/9, age=49±9yr, range 37-62yr) were scanned on a 3T whole-body scanner for prospective and retrospective studies, respectively, using both T1-weighted spin-echo (SE) and T2-weighted fast spin-echo (FSE) sequences. We proposed a network which we term "X-net" to reconstruct both T1- and T2-weighted images from down-sampled images as well as a network termed "Y-net" which reconstructs T2-weighted images from highly down-sampled T2-weighted images and fully sampled T1-weighted images. Both X-net and Y-net are composed of two concatenated subnetworks. We investigate optimal sampling patterns, the optimal patch size for augmentation, and the optimal acceleration factors for network training. An additional Y-net combined with a generative adversarial network (GAN) was also implemented and tested to investigate the effects of the GAN on the Y-net performance. Single- and joint-reconstruction parallel-imaging and compressed-sensing algorithms along with a conventional U-net were also tested and compared with the proposed networks. For this comparison, the structural similarity (SSIM), normalized mean square error (NMSE), and Fréchet inception distance (FID) were calculated between the outputs of the networks and fully sampled images. The statistical significance of the performance was evaluated by assessing the interclass correlation and in paired t-tests. The outputs from the two concatenated subnetworks were closer to the fully sampled images compared to those from one subnetwork, with this result showing statistical significance. Uniform down-sampling led to a statically significant improvement in the image quality compared to random or central down-sampling patterns. In addition, the proposed networks provided higher SSIM and NMSE values than U-net, compressed-sensing, and parallel-imaging algorithms, all at statistically significant levels. The GAN-based Y-net showed a better FID and more realistic images compared to a non-GAN-based Y-net. The performance capabilities of the networks were similar between normal subjects and patients. The proposed X-net and Y-net effectively reconstructed full images from down-sampled images, outperforming the conventional parallel-imaging, compressed-sensing and U-net methods and providing more realistic images in combination with a GAN. The developed networks potentially enable us to accelerate multicontrast anatomical MR imaging in routine clinical studies including T1-and T2-weighted imaging.

Simultaneous use of individual and joint regularization terms in compressive sensing: Joint reconstruction of multi-channel multi-contrast MRI acquisitions.

Multi-contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time-efficient strategy to acquire high-quality multi-contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage-of-features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi-channel multi-contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single-channel simulated and multi-channel in-vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in-vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage-of-features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use.

Segment-specific progression of carotid artery atherosclerosis: a magnetic resonance vessel wall imaging study.

This study aimed to investigate the segment-specific progression of atherosclerotic carotid plaques using serial multi-contrast magnetic resonance (MR) imaging. Symptomatic patients with carotid 30-70% stenosis were recruited and underwent carotid MR vessel wall imaging at baseline and follow-up time point (≥ 6months after baseline). The location of plaques was determined according to the maximum wall thickness located above or below carotid bifurcation. The baseline and changing characteristics of carotid plaques were compared between plaques above and below carotid bifurcation, and the risk factors for segment-specific plaque progression were analyzed with logistic regression. Ninety-six carotid plaques from 73 patients (mean age 66.5 ± 11.4years old) were eligible for statistical analysis. Compared with plaques located below carotid bifurcation, those above bifurcation had significantly greater stenosis at baseline (57.2 ± 13.0% vs. 50.4 ± 13.5%, p = 0.016, adjusted p = 0.005) and greater progression rate of carotid wall volume (35.2 ± 68.8mm3/year vs. 4.2 ± 65.0mm3/year, p = 0.026, adjusted p = 0.005) before and after adjusting for all clinical risk factors and baseline stenosis and wall volume of carotid arteries. Logistic regression showed that the related risk factors were age, hypertension, and smoke for the progression of plaques located above the bifurcation and age for plaques below the bifurcation, respectively. Plaques located above the bifurcation of carotid arteries had greater annual progression and correlated with more cardiovascular risk factors compared with those located below the bifurcation.

Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning

BackgroundMagnetic resonance images with multiple contrasts or sequences are commonly used for segmenting brain tissues, including lesions, in multiple sclerosis (MS). However, acquisition of images with multiple contrasts increases the scan time and complexity of the analysis, possibly introducing factors that could compromise segmentation quality. ObjectiveTo investigate the effect of various combinations of multi-contrast images as input on the segmented volumes of gray (GM) and white matter (WM), cerebrospinal fluid (CSF), and lesions using a deep neural network. MethodsU-net, a fully convolutional neural network was used to automatically segment GM, WM, CSF, and lesions in 1000 MS patients. The input to the network consisted of 15 combinations of FLAIR, T1-, T2-, and proton density-weighted images. The Dice similarity coefficient (DSC) was evaluated to assess the segmentation performance. For lesions, true positive rate (TPR) and false positive rate (FPR) were also evaluated. In addition, the effect of lesion size on lesion segmentation was investigated. ResultsHighest DSC was observed for all the tissue volumes, including lesions, when the input was combination of all four image contrasts. All other input combinations that included FLAIR also provided high DSC for all tissue classes. However, the quality of lesion segmentation showed strong dependence on the input images. The DSC and TPR values for inputs with the four contrast combination and FLAIR alone were very similar, but FLAIR showed a moderately higher FPR for lesion size <100 μl. For lesions smaller than 20 μl all image combinations resulted in poor performance. The segmentation quality improved with lesion size. ConclusionsBest performance for segmented tissue volumes was obtained with all four image contrasts as the input, and comparable performance was attainable with FLAIR only as the input, albeit with a moderate increase in FPR for small lesions. This implies that acquisition of only FLAIR images provides satisfactory tissue segmentation. Lesion segmentation was poor for very small lesions and improved rapidly with lesion size.

Four Different Carotid Atherosclerotic Behaviors Based on Luminal Stenosis and Plaque Characteristics in Symptomatic Patients: An in Vivo Study.

Correct stratification of ischemic stroke risk allows for the proper treatment of carotid atherosclerotic disease. We seek to differentiate plaque types based on stenosis level and plaque morphology. The Chinese Atherosclerosis Risk Evaluation (CARE–II) study is a cross-sectional, observational, multicenter study to assess carotid atherosclerotic plaques in symptomatic subjects using vessel wall magnetic resonance imaging. Plaque morphology and presence of plaque components were reviewed using multi-contrast magnetic resonance imaging. The carotid arteries were divided into four groups based on stenosis level and plaque components. Out of 1072 ischemic stroke subjects, 452 ipsilateral side carotid arteries were included. Significant stenosis (SS) (≥50% stenosis) with high-risk plaque (HRP) features was present in 37 arteries (8.2%), SS(+)/HRP(−) in 29 arteries (6.4%), SS(−)/HRP(+) in 57 arteries (12.6%), and SS(−)/HRP(−) in 329 arteries (72.8%). The prevalence of SS(−)/HRP(+) arteries in this cohort was substantial and had greater wall thickness than the SS(+)/HRP(−) group. These arteries may be misclassified for carotid revascularization by current guidelines based on the degree of luminal stenosis only. These findings have implications for further studies to assess stroke risk using vessel wall imaging.

Low-Rank Tensor Models for Improved Multi-Dimensional MRI: Application to Dynamic Cardiac T 1 Mapping.

Multi-dimensional, multi-contrast magnetic resonance imaging (MRI) has become increasingly available for comprehensive and time-efficient evaluation of various pathologies, providing large amounts of data and offering new opportunities for improved image reconstructions. Recently, a cardiac phase-resolved myocardial T 1 mapping method has been introduced to provide dynamic information on tissue viability. Improved spatio-temporal resolution in clinically acceptable scan times is highly desirable but requires high acceleration factors. Tensors are well-suited to describe inter-dimensional hidden structures in such multi-dimensional datasets. In this study, we sought to utilize and compare different tensor decomposition methods, without the use of auxiliary navigator data. We explored multiple processing approaches in order to enable high-resolution cardiac phase-resolved myocardial T 1 mapping. Eight different low-rank tensor approximation and processing approaches were evaluated using quantitative analysis of accuracy and precision in T 1 maps acquired in six healthy volunteers. All methods provided comparable T 1 values. However, the precision was significantly improved using local processing, as well as a direct tensor rank approximation. Low-rank tensor approximation approaches are well-suited to enable dynamic T 1 mapping at high spatio-temporal resolutions.

Coupled Dictionary Learning for Multi-Contrast MRI Reconstruction.

Magnetic resonance (MR) imaging tasks often involve multiple contrasts, such as T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities in either structure level or gray level. In this paper, we propose a coupled dictionary learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage the dependency correlation between different contrasts for guided or joint reconstruction from their under-sampled k -space data. Our approach iterates between three stages: coupled dictionary learning, coupled sparse denoising, and enforcing k -space consistency. The first stage learns a set of dictionaries that not only are adaptive to the contrasts, but also capture correlations among multiple contrasts in a sparse transform domain. By capitalizing on the learned dictionaries, the second stage performs coupled sparse coding to remove the aliasing and noise in the corrupted contrasts. The third stage enforces consistency between the denoised contrasts and the measurements in the k -space domain. Numerical experiments, consisting of retrospective under-sampling of various MRI contrasts with a variety of sampling schemes, demonstrate that CDLMRI is capable of capturing structural dependencies between different contrasts. The learned priors indicate notable advantages in multi-contrast MR imaging and promising applications in quantitative MR imaging such as MR fingerprinting.

Using 3D Thin-Layer Model with in Vivo Patient-Specific Vessel Material Properties to Assesse Carotid Atherosclerotic Plaque Vulnerability

Image-based computational models have been introduced to calculate plaque stress/strain conditions and investigate their association with plaque progression and rupture [Tang, Yang, Zheng, et al. (2004)]. However, the accuracy of the computational results is heavily dependent on the data and assumptions used by those models. Patient-specific vessel material properties are in general lacking in image-based computational models, limiting the accuracy of their stress/strain calculations. A noninvasive approach of combining in vivo 3D multi-contrast and Cine magnetic resonance imaging (MRI) and computational modeling was used to quantify patient-specific carotid plaque material properties for potential plaque model improvements [Wang, Canton, Guo, et al. (2017)]. The stress-based plaque vulnerability index (SPVI) was proposed to combine mechanical analysis, plaque morphology and composition for more complete carotid plaque vulnerability assessment.

Detection and clinical correlation of leukocortical lesions in pediatric-onset multiple sclerosis on multi-contrast MRI

Objective: To determine the frequency of cortical lesions (CLs) in patients with pediatric-onset multiple sclerosis (POMS) using multi-contrast magnetic resonance imaging (MRI), and the relationship between frontal CL load and upper limb dexterity assessed with the Nine-Hole Peg Test (9-HPT). Methods: Participants completed the 9-HPT and were imaged on a 3T MRI scanner to collect T1-weighted three-dimensional (3D) magnetization prepared rapid gradient echo (MPRAGE), proton density–weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) images. CLs were manually segmented using all MRI contrasts. Results: We enrolled 24 participants with POMS (mean (standard deviation) age at first symptom: 13.3 (±2.7) years; mean age at scan: 18.8 (±3) years; mean disease duration of 5 (±3.2) years). A total of 391 CLs (mean, 16.3 ± 27.2; median, 7) were identified in 19 of 24 POMS patients (79%). The total number of CLs was positively associated with white matter lesion volume (p = 0.04) but not with thalamic volume, age at the time of the scan, or disease duration. The number of frontal CLs was associated with slower performance on the 9-HPT (p = 0.05). Conclusion: Multi-contrast 3T MRI led to a high rate of CL detection, demonstrating that cortical pathology occurs even in pediatric-onset disease. Frontal lobe CL count was associated with reduced manual dexterity, indicating that these CLs are clinically relevant.

Joint sparse reconstruction of multi-contrast MRI images with graph based redundant wavelet transform

BackgroundMulti-contrast images in magnetic resonance imaging (MRI) provide abundant contrast information reflecting the characteristics of the internal tissues of human bodies, and thus have been widely utilized in clinical diagnosis. However, long acquisition time limits the application of multi-contrast MRI. One efficient way to accelerate data acquisition is to under-sample the k-space data and then reconstruct images with sparsity constraint. However, images are compromised at high acceleration factor if images are reconstructed individually. We aim to improve the images with a jointly sparse reconstruction and Graph-based redundant wavelet transform (GBRWT).MethodsFirst, a sparsifying transform, GBRWT, is trained to reflect the similarity of tissue structures in multi-contrast images. Second, joint multi-contrast image reconstruction is formulated as a ℓ2, 1 norm optimization problem under GBRWT representations. Third, the optimization problem is numerically solved using a derived alternating direction method.ResultsExperimental results in synthetic and in vivo MRI data demonstrate that the proposed joint reconstruction method can achieve lower reconstruction errors and better preserve image structures than the compared joint reconstruction methods. Besides, the proposed method outperforms single image reconstruction with joint sparsity constraint of multi-contrast images.ConclusionsThe proposed method explores the joint sparsity of multi-contrast MRI images under graph-based redundant wavelet transform and realizes joint sparse reconstruction of multi-contrast images. Experiment demonstrate that the proposed method outperforms the compared joint reconstruction methods as well as individual reconstructions. With this high quality image reconstruction method, it is possible to achieve the high acceleration factors by exploring the complementary information provided by multi-contrast MRI.

Association of Age and Size of Carotid Artery Intraplaque Hemorrhage and Minor Fibrous Cap Disruption: A High Resolution Magnetic Resonance Imaging Study.

Aim: To investigate the association between the volumes of different aging intraplaque hemorrhage (IPH) and minor fibrous cap disruption (MFCD) in carotid arteries.Methods: Patients with cerebrovascular symptoms and carotid atherosclerotic plaques determined by ultrasound were recruited and underwent multi-contrast magnetic resonance (MR) vessel wall imaging for carotid arteries. Carotid plaques with IPH on MR imaging were included in the analysis. The age (fresh or recent) and the volume of IPH for each plaque were evaluated.Results: In total, 41 carotid plaques in 37 patients (mean age 70.2 ± 11.0 years old; 32 males) were eligible for statistical analysis. The absolute volume of fresh IPH in plaques with MFCD was significantly larger than that in plaques without MFCD (109.83 ± 75.49 mm3 vs. 30.54 ± 20.62 mm3, P = 0.002). Logistic regression showed that the absolute volume of fresh IPH was significantly associated with MFCD before (odds ratio [OR], 1.735; 95% confidence interval [CI], 1.127–2.670; P = 0.012) and after adjusting for confounding factors (OR, 1.823; 95% CI, 1.076–3.090; P = 0.026). There was no significant association between recent IPH volume and MFCD (P > 0.05).Conclusion: The volume of fresh IPH is independently associated with MFCD in carotid plaques, suggesting that integrity of fibrous cap may change with different age and size of IPH.

A Deep Information Sharing Network for Multi-Contrast Compressed Sensing MRI Reconstruction.

Compressed sensing (CS) theory can accelerate multi-contrast magnetic resonance imaging (MRI) by sampling fewer measurements within each contrast. However, conventional optimization-based reconstruction models suffer several limitations, including a strict assumption of shared sparse support, time-consuming optimization, and "shallow" models with difficulties in encoding the patterns contained in massive MRI data. In this paper, we propose the first deep learning model for multi-contrast CS-MRI reconstruction. We achieve information sharing through feature sharing units, which significantly reduces the number of model parameters. The feature sharing unit combines with a data fidelity unit to comprise an inference block, which are then cascaded with dense connections, allowing for efficient information transmission across different depths of the network. Experiments on various multi-contrast MRI datasets show that the proposed model outperforms both state-of-the-art single-contrast and multi-contrast MRI methods in accuracy and efficiency. We demonstrate that improved reconstruction quality can bring benefits to subsequent medical image analysis. Furthermore, the robustness of the proposed model to misregistration shows its potential in real MRI applications.

Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art

Anatomically realistic volume conductor models of the human head are important for accurate forward modeling of the electric field during transcranial brain stimulation (TBS), electro- (EEG) and magnetoencephalography (MEG). In particular, the skull compartment exerts a strong influence on the field distribution due to its low conductivity, suggesting the need to represent its geometry accurately. However, automatic skull reconstruction from structural magnetic resonance (MR) images is difficult, as compact bone has a very low signal in magnetic resonance imaging (MRI). Here, we evaluate three methods for skull segmentation, namely FSL BET2, the unified segmentation routine of SPM12 with extended spatial tissue priors, and the skullfinder tool of BrainSuite. To our knowledge, this study is the first to rigorously assess the accuracy of these state-of-the-art tools by comparison with CT-based skull segmentations on a group of ten subjects. We demonstrate several key factors that improve the segmentation quality, including the use of multi-contrast MRI data, the optimization of the MR sequences and the adaptation of the parameters of the segmentation methods. We conclude that FSL and SPM12 achieve better skull segmentations than BrainSuite. The former methods obtain reasonable results for the upper part of the skull when a combination of T1- and T2-weighted images is used as input. The SPM12-based results can be improved slightly further by means of simple morphological operations to fix local defects. In contrast to FSL BET2, the SPM12-based segmentation with extended spatial tissue priors and the BrainSuite-based segmentation provide coarse reconstructions of the vertebrae, enabling the construction of volume conductor models that include the neck. We exemplarily demonstrate that the extended models enable a more accurate estimation of the electric field distribution during transcranial direct current stimulation (tDCS) for montages that involve extraencephalic electrodes. The methods provided by FSL and SPM12 are integrated into pipelines for the automatic generation of realistic head models based on tetrahedral meshes, which are distributed as part of the open-source software package SimNIBS for field calculations for transcranial brain stimulation.