The image contrast in magnetic resonance imaging (MRI) is highly sensitive to several mechanisms that are modulated by the properties of the tissue environment. The degree and type of contrast weighting may be viewed as image filters that accentuate specific tissue properties. Maps of quantitative measures of these mechanisms, akin to microstructural/environmental-specific tissue stains, may be generated to characterize the MRI and physiological properties of biological tissues. In this paper, three quantitative MRI (qMRI) methods for characterizing white matter microstructural properties are reviewed. All of these measures measure complementary aspects of how water interacts with the tissue environment. Diffusion MRI including diffusion tensor imaging characterizes the diffusion of water in the tissues and is sensitive to the microstructural density, spacing and orientational organization of tissue membranes including myelin. Magnetization transfer imaging characterizes the amount and degree of magnetization exchange between free water and macromolecules like proteins found in the myelin bilayers. Relaxometery measures the MRI relaxation constants T1 and T2, which in white matter has a component associated with the water trapped in the myelin bilayers. The conduction of signals between distant brain regions occurs primarily through myelinated white matter tracts, thus these methods are potential indicators of pathology and structural connectivity in the brain. This paper provides an overview of the qMRI stain mechanisms, acquisition and analysis strategies, and applications for these qMRI stains.

Purpose: To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint. Methods: A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts. Results: The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions. Conclusion: The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging.

Purpose To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images. Materials and Methods A fully automated deep learning-based cartilage lesion detection system was developed by using segmentation and classification convolutional neural networks (CNNs). Fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain were retrospectively analyzed by using the deep learning method. The reference standard for training the CNN classification was the interpretation provided by a fellowship-trained musculoskeletal radiologist of the presence or absence of a cartilage lesion within 17 395 small image patches placed on the articular surfaces of the femur and tibia. Receiver operating curve (ROC) analysis and the κ statistic were used to assess diagnostic performance and intraobserver agreement for detecting cartilage lesions for two individual evaluations performed by the cartilage lesion detection system. Results The sensitivity and specificity of the cartilage lesion detection system at the optimal threshold according to the Youden index were 84.1% and 85.2%, respectively, for evaluation 1 and 80.5% and 87.9%, respectively, for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively, indicating high overall diagnostic accuracy for detecting cartilage lesions. There was good intraobserver agreement between the two individual evaluations, with a κ of 0.76. Conclusion This study demonstrated the feasibility of using a fully automated deep learning-based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury. © RSNA, 2018 Online supplemental material is available for this article .

A novel method for iterative reconstruction of images from undersampled MRI data acquired by multiple receiver coil systems is presented. Based on Projection onto Convex Sets (POCS) formalism, the method for SENSitivity Encoded data reconstruction (POCSENSE) can be readily modified to include various linear and nonlinear reconstruction constraints. Such constraints may be beneficial for reconstructing highly and overcritically undersampled data sets to improve image quality. POCSENSE is conceptually simple and numerically efficient and can reconstruct images from data sampled on arbitrary k-space trajectories. The applicability of POCSENSE for image reconstruction with nonlinear constraining was demonstrated using a wide range of simulated and real MRI data.

Noninvasive biomarkers of intracellular accumulation of fat within the liver (hepatic steatosis) are urgently needed for detection and quantitative grading of nonalcoholic fatty liver disease, the most common cause of chronic liver disease in the United States. Accurate quantification of fat with MRI is challenging due the presence of several confounding factors, including T* 2 decay. The specific purpose of this work is to quantify the impact of T* 2 decay and develop a multiexponential T* 2 correction method for improved accuracy of fat quantification, relaxing assumptions made by previous T* 2 correction methods. A modified Gauss-Newton algorithm is used to estimate the T* 2 for water and fat independently. Improved quantification of fat is demonstrated, with independent estimation of T* 2 for water and fat using phantom experiments. The tradeoffs in algorithm stability and accuracy between multiexponential and single exponential techniques are discussed. Magn Reson Med 63: [849][850][851][852][853][854][855][856][857] 2010. V C 2010 Wiley-Liss, Inc. Key words: fat quantification; chemical shift imaging; T 2 * decay; hepatic steatosis; IDEAL Nonalcoholic fatty liver disease (NAFLD) is now recognized as the most common cause of chronic liver disease, afflicting up to 30% of all Americans (1). It is an emerging condition closely related to obesity and insulin resistance. Importantly, NAFLD's prevalence among children is reported to be up to 10% overall, and as high as 53% in obese children (2-4). NAFLD is expected by many experts to become a leading cause of end-stage liver disease as the prevalence of obesity increases in the general population, both in the US and worldwide.The hallmark feature of NAFLD is intracellular accumulation of triglycerides within hepatocytes (steatosis). In many patients, steatosis leads to inflammation and fibrosis, and ultimately to cirrhosis, with subsequent liver failure or development of hepatocellular carcinoma. In such patients, liver transplant is the only definitive option for cure. Nontargeted liver biopsy, which is the current gold standard for diagnosis of NAFLD, is limited by its high cost, morbidity, and importantly, its high sampling variability due to the heterogeneous nature of intracellular lipid accumulation. Quantitative assessment of liver fat using MRI is attractive because it can assess fat over the entire liver, thus avoiding the sampling variability, as well as the risks and high cost of biopsy.Chemical shift-based MRI techniques are currently under development by many groups for the quantification of liver fat (5-10). These methods exploit the differences in chemical shift between water and fat (3.29 ppm between water and the main resonance peak of fat, 210 Hz at 1.5 T). Chemical shift-based methods are often used to estimate the concentration of triglycerides through the use of the fat fraction, which is independent of amplitude of radiofrequency field coil sensitivities and therefore is a useful metric of fat concentration (11).Two-point methods acquire tw...

MR parameter mapping requires sampling along additional (parametric) dimension, which often limits its clinical appeal due to a several-fold increase in scan times compared to conventional anatomic imaging. Data undersampling combined with parallel imaging is an attractive way to reduce scan time in such applications. However, inherent SNR penalties of parallel MRI due to noise amplification often limit its utility even at moderate acceleration factors, requiring regularization by prior knowledge. In this work, we propose a novel regularization strategy, which utilizes smoothness of signal evolution in the parametric dimension within compressed sensing framework (p-CS) to provide accurate and precise estimation of parametric maps from undersampled data. The performance of the method was demonstrated with variable flip angle T1 mapping and compared favorably to two representative reconstruction approaches, image space-based total variation regularization and an analytical model-based reconstruction. The proposed p-CS regularization was found to provide efficient suppression of noise amplification and preservation of parameter mapping accuracy without explicit utilization of analytical signal models. The developed method may facilitate acceleration of quantitative MRI techniques that are not suitable to model-based reconstruction because of complex signal models or when signal deviations from the expected analytical model exist.

).q RSNA, 2014 Purpose:To evaluate the clinical utility of fast whole-brain macromolecular proton fraction (MPF) mapping in multiple sclerosis (MS) and compare MPF with established quantitative magnetic resonance (MR) imaging measures of tissue damage including magnetization transfer (MT) ratio and relaxation rate (R1). Materials and Methods:In this institutional review board-approved and HIPAAcompliant study, 14 healthy control participants, 18 relapsing-remitting MS (RRMS) patients, and 12 secondary progressive MS (SPMS) patients provided written informed consent and underwent 3-T MR imaging. Threedimensional MPF maps were reconstructed from MTweighted images and R1 maps by the single-point method.Mean MPF, R1, and MT ratio in normal-appearing white matter (WM), gray matter (GM), and lesions were compared between subject groups by using analysis of variance. Results:RRMS patients had lower WM and GM MPF than controls, with percentage decreases of 6.5% (P , .005) and 5.4% (P , .05). MPF in SPMS was reduced relative to RRMS in WM, GM, and lesions by 6.4% (P , .005), 13.4% (P , .005), and 11.7% (P , .05), respectively. EDSS and MSFC demonstrated strongest correlations with MPF in GM (r = 20.74 and 0.81; P , .001) followed by WM (r = 20.57 and 0.72; P , .01) and lesions (r = 20.42 and 0.50; P , .05). R1 and MT ratio in all tissues were significantly less correlated with clinical scores than GM MPF (P , .05). Conclusion:MPF mapping enables quantitative assessment of demyelination in normal-appearing brain tissues and shows primary clinical relevance of GM damage in MS. MPF outperforms MT ratio and R1 in detection of MS-related tissue changes.q RSNA, 2014

We present MRiLab, a new comprehensive simulator for large-scale realistic MRI simulations on a regular PC equipped with a modern graphical processing unit (GPU). MRiLab combines realistic tissue modeling with numerical virtualization of an MRI system and scanning experiment to enable assessment of a broad range of MRI approaches including advanced quantitative MRI methods inferring microstructure on a sub-voxel level. A flexibl representation of tissue microstructure is achieved in MRiLab by employing the generalized tissue model with multiple exchanging water and macromolecular proton pools rather than a system of independent proton isochromats typically used in previous simulators. The computational power needed for simulation of the biologically relevant tissue models in large 3D objects is gained using parallelized execution on GPU. Three simulated and one actual MRI experiments were performed to demonstrate the ability of the new simulator to accommodate a wide variety of voxel composition scenarios and demonstrate detrimental effects of simplifie treatment of tissue micro-organization adapted in previous simulators. GPU execution allowed ∼200× improvement in computational speed over standard CPU. As a cross-platform, open-source, extensible environment for customizing virtual MRI experiments, MRiLab streamlines the development of new MRI methods, especially those aiming to infer quantitatively tissue composition and microstructure.