Using MRI to quantify skeletal muscle pathology in Duchenne muscular dystrophy: A systematic mapping review.

There is a great demand for accurate non-invasive measures to better define the natural history of disease progression or treatment outcome in Duchenne muscular dystrophy (DMD) and to facilitate the inclusion of a large range of participants in DMD clinical trials. This review aims to investigate which MRI sequences and analysis methods have been used and to identify future needs. Medline, Embase, Scopus, Web of Science, Inspec, and Compendex databases were searched up to 2 November 2019, using keywords "magnetic resonance imaging" and "Duchenne muscular dystrophy." The review showed the trend of using T1w and T2w MRI images for semi-qualitative inspection of structural alterations of DMD muscle using a diversity of grading scales, with increasing use of T2map, Dixon, and MR spectroscopy (MRS). High-field (>3T) MRI dominated the studies with animal models. The quantitative MRI techniques have allowed a more precise estimation of local or generalized disease severity. Longitudinal studies assessing the effect of an intervention have also become more prominent, in both clinical and animal model subjects. Quality assessment of the included longitudinal studies was performed using the Newcastle-Ottawa Quality Assessment Scale adapted to comprise bias in selection, comparability, exposure, and outcome. Additional large clinical trials are needed to consolidate research using MRI as a biomarker in DMD and to validate findings against established gold standards. This future work should use a multiparametric and quantitative MRI acquisition protocol, assess the repeatability of measurements, and correlate findings to histologic parameters.

Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test.

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual's plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set.

Muscle percentage index as a marker of disease severity in golden retriever muscular dystrophy.

INTRODUCTION: Golden retriever muscular dystrophy (GRMD) is a spontaneous X-linked canine model of Duchenne muscular dystrophy that resembles the human condition. Muscle percentage index (MPI) is proposed as an imaging biomarker of disease severity in GRMD. METHODS: To assess MPI, we used MRI data acquired from nine GRMD samples using a 4.7 T small-bore scanner. A machine learning approach was used with eight raw quantitative mapping of MRI data images (T1m, T2m, two Dixon maps, and four diffusion tensor imaging maps), three types of texture descriptors (local binary pattern, gray-level co-occurrence matrix, gray-level run-length matrix), and a gradient descriptor (histogram of oriented gradients). RESULTS: The confusion matrix, averaged over all samples, showed 93.5% of muscle pixels classified correctly. The classification, optimized in a leave-one-out cross-validation, provided an average accuracy of 80% with a discrepancy in overestimation for young (8%) and old (20%) dogs. DISCUSSION: MPI could be useful for quantifying GRMD severity, but careful interpretation is needed for severe cases.

Texture as an imaging biomarker for disease severity in golden retriever muscular dystrophy.

INTRODUCTION: Golden retriever muscular dystrophy (GRMD), an X-linked recessive disorder, causes similar phenotypic features to Duchenne muscular dystrophy (DMD). There is currently a need for a quantitative and reproducible monitoring of disease progression for GRMD and DMD. METHODS: To assess severity in the GRMD, we analyzed texture features extracted from multi-parametric MRI (T1w, T2w, T1m, T2m, and Dixon images) using 5 feature extraction methods and classified using support vector machines. RESULTS: A single feature from qualitative images can provide 89% maximal accuracy. Furthermore, 2 features from T1w, T2m, or Dixon images provided highest accuracy. When considering a tradeoff between scan-time and computational complexity, T2m images provided good accuracy at a lower acquisition and processing time and effort. CONCLUSIONS: The combination of MRI texture features improved the classification accuracy for assessment of disease progression in GRMD with evaluation of the heterogenous nature of skeletal muscles as reflection of the histopathological changes. Muscle Nerve 59:380-386, 2019.

Implementation of High-Performance Correlation and Mapping Engine for Rapid Generation of Brain Connectivity Networks from Big fMRI Data.

With the emergence of the dynamic functional connectivity analysis, and the studies relying on real-time neurological feedback, the need for rapid processing methods becomes even more critical. Seed-based Correlation Analysis (SCA) of fMRI data has been used to create brain connectivity networks. With close to a million voxels in a fMRI dataset, the number of calculations involved in SCA becomes high. This work aims to demonstrate a new approach which produces high-resolution brain connectivity maps rapidly. The results show that HPCME with four FPGAs can improve the SCA processing speed by a factor of 40 or more over that of a PC workstation with a multicore CPU.

MARBLES - Metal Artifact Based Landmark Enhanced Susceptibility Weighted Imaging For Interventional Device Localization In MRI.

Susceptibility Weighted Imaging (SWI) is a method extensively studied for its application to improve contrast in MR imaging modality. The method enhances the visualization of magnetically susceptible content such as iron, calcium and zinc in the tissues by using the susceptibility differences in tissues to generate a unique image contrast. In this study, we propose an SWI based approach to improve the visualization of interventional devices in MRI data. Results obtained from two datasets (biopsy needle and brachytherapy seeds), indicate SWI to be suitable for visualization of the interventional devices, while also being computationally faster when compared with quantitative susceptibility mapping (QSM).

Three-dimensional self-gated cardiac MR imaging for the evaluation of myocardial infarction in mouse model on a 3T clinical MR system.

PURPOSE: To develop and assess a three-dimensional (3D) self-gated technique for the evaluation of myocardial infarction (MI) in mouse model without the use of external electrocardiogram (ECG) trigger and respiratory motion sensor on a 3T clinical MR system. METHODS: A 3D T1-weighted GRE sequence with stack-of-stars sampling trajectories was developed and performed on six mice with MIs that were injected with a gadolinium-based contrast agent at a 3T clinical MR system. Respiratory and cardiac self-gating signals were derived from the Cartesian mapping of the k-space center along the partition encoding direction by bandpass filtering in image domain. The data were then realigned according to the predetermined self-gating signals for the following image reconstruction. In order to accelerate the data acquisition, image reconstruction was based on compressed sensing (CS) theory by exploiting temporal sparsity of the reconstructed images. In addition, images were also reconstructed from the same realigned data by conventional regridding method for demonstrating the advantageous of the proposed reconstruction method. Furthermore, the accuracy of detecting MI by the proposed method was assessed using histological analysis as the standard reference. Linear regression and Bland-Altman analysis were used to assess the agreement between the proposed method and the histological analysis. RESULTS: Compared to the conventional regridding method, the proposed CS method reconstructed images with much less streaking artifact, as well as a better contrast-to-noise ratio (CNR) between the blood and myocardium (4.1 ± 2.1 vs. 2.9 ± 1.1, p = 0.031). Linear regression and Bland-Altman analysis demonstrated that excellent correlation was obtained between infarct sizes derived from the proposed method and histology analysis. CONCLUSION: A 3D T1-weighted self-gating technique for mouse cardiac imaging was developed, which has potential for accurately evaluating MIs in mice at 3T clinical MR system without the use of external ECG trigger and respiratory motion sensor.

Parallel compressive sensing in a hybrid space: Application in interventional MRI.

Recently, susceptibility based positive contrast MRI technique emerged as an effective method of visualizing the small MR compatible devices, such as brachytherapy seeds. One of the challenges associated with this method is the long scan time. In this work, we present an accelerated susceptibility based positive contrast MR imaging method, in which the susceptibility map can be generated from an under-sampled data. We use a combination of parallel imaging (GRAPPA) and compressive sensing (CS) technique in a hybrid k-space. The results in brachytherapy seeds imaging show that 3-D high-quality images can be achieved at an acceleration factor up to 4, which can decrease the scan time from 4 minutes to 1.4 minutes.

Black-blood T2* mapping with delay alternating with nutation for tailored excitation.

PURPOSE: To develop a black-blood T2* mapping method using a Delay Alternating with Nutation for Tailored Excitation (DANTE) preparation combined with a multi-echo gradient echo (GRE) readout (DANTE-GRE). MATERIALS AND METHODS: Simulations of the Bloch equation for DANTE-GRE were performed to optimize sequence parameters. After optimization, the sequence was applied to a phantom scan and to neck and lower extremity scans conducted on 12 volunteers at 3T using DANTE-GRE, Motion-Sensitized Driven Equilibrium (MSDE)-GRE, and multi-echo GRE. T2* values were measured using an offset model. Statistical analyses were conducted to compare the T2* values between the three sequences. RESULTS: Simulation results showed that blood suppression can be achieved with various DANTE parameter adjustments. T2* maps acquired by DANTE-GRE were consistent and comparable to those acquired with multi-echo GRE in phantom experiments. In the in vivo experiments, DANTE-GRE was more comparable to multi-echo GRE than MSDE-GRE regarding the measurement of muscle T2* values. CONCLUSION: Due to its high signal intensity retention and effective blood signal suppression, DANTE-GRE allows for robust and accurate T2* quantification, superior to that of MSDE-GRE, while overcoming blood flow artifacts associated with traditional multi-echo GRE.

Compressed sensing MRI reconstruction from 3D multichannel data using GPUs.

PURPOSE: To accelerate iterative reconstructions of compressed sensing (CS) MRI from 3D multichannel data using graphics processing units (GPUs). METHODS: The sparsity of MRI signals and parallel array receivers can reduce the data acquisition requirements. However, iterative CS reconstructions from data acquired using an array system may take a significantly long time, especially for a large number of parallel channels. This paper presents an efficient method for CS-MRI reconstruction from 3D multichannel data using GPUs. In this method, CS reconstructions were simultaneously processed in a channel-by-channel fashion on the GPU, in which the computations of multiple-channel 3D-CS reconstructions are highly parallelized. The final image was then produced by a sum-of-squares method on the central processing unit. Implementation details including algorithm, data/memory management, and parallelization schemes are reported in the paper. RESULTS: Both simulated data and in vivo MRI array data were tested. The results showed that the proposed method can significantly improve the image reconstruction efficiency, typically shortening the runtime by a factor of 30. CONCLUSIONS: Using low-cost GPUs and an efficient algorithm allowed the 3D multislice compressive-sensing reconstruction to be performed in less than 1 s. The rapid reconstructions are expected to help bring high-dimensional, multichannel parallel CS MRI closer to clinical applications. Magn Reson Med 78:2265-2274, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Susceptibility-based positive contrast MRI of brachytherapy seeds.

PURPOSE: To provide visualization of the brachytherapy seeds and differentiation with natural structures in MRI by taking advantage of their high magnetic susceptibility to generate positive-contrast images. METHODS: The method is based on mapping the susceptibility using an equivalent short-TE sequence and a kernel deconvolution algorithm with a regularized L1 minimization. An appealing aspect of the method is that signals from the surrounding areas where signal to noise ratio (SNR) is sufficiently high are used to derive the susceptibility of the seeds, even though the SNR in the immediate vicinity of the seeds can be extremely low due to rapid signal decay. RESULTS: The method is tested using computer simulations and experimental data. Comparing to conventional methods, the proposed method improves seed definition by a factor of >70% in the experiments. It produces the enhanced contrast at the exact seed location, whereas methods based on susceptibility gradient mapping produce highlighted regions surrounding the seeds. The proposed method is capable to perform the function for a wide range of resolutions and SNRs. CONCLUSION: The results show that the proposed method provides positive contrast for the seeds and correctly differentiates them from other structures that appear similar to the seeds on conventional magnitude images.

Improving multi-channel compressed sensing MRI with reweighted l 1 minimization.

Integrating compressed sensing (CS) and parallel imaging (PI) with multi-channel receiver has proven to be an effective technology to speed up magnetic resonance imaging (MRI). In this paper, we propose a method that extends the reweighted l 1 minimization to the CS-MRI with multi-channel data. The method applies a reweighted l 1 minimization algorithm to reconstruct each channel image, and then generates the final image by a sum-of-squares method. Computer simulations based on synthetic data and in vivo MRI imaging data show that the new method can improve the reconstruction quality at a slightly increased computation cost.

Segmentation, Inference and Classification of Partially Overlapping Nanoparticles.

This paper presents a method that enables automated morphology analysis of partially overlapping nanoparticles in electron micrographs. In the undertaking of morphology analysis, three tasks appear necessary: separate individual particles from an agglomerate of overlapping nano-objects; infer the particle's missing contours; and ultimately, classify the particles by shape based on their complete contours. Our specific method adopts a two-stage approach: the first stage executes the task of particle separation, and the second stage conducts simultaneously the tasks of contour inference and shape classification. For the first stage, a modified ultimate erosion process is developed for decomposing a mixture of particles into markers, and then, an edge-to-marker association method is proposed to identify the set of evidences that eventually delineate individual objects. We also provided theoretical justification regarding the separation capability of the first stage. In the second stage, the set of evidences become inputs to a Gaussian mixture model on B-splines, the solution of which leads to the joint learning of the missing contour and the particle shape. Using twelve real electron micrographs of overlapping nanoparticles, we compare the proposed method with seven state-of-the-art methods. The results show the superiority of the proposed method in terms of particle recognition rate.

Compressive sensing imaging with randomized lattice sampling: applications to fast 3D MRI.

Fast MRI makes it possible to visualize dynamic biological phenomena and can potentially reduce the cost of diagnostic imaging. Constrained imaging methods such as compressive sense (CS) and optimal lattice sampling (OLS) have proven to be effective for speeding up MRI. In doing so, CS takes advantage of the image sparsity or compressibility and OLS utilizes the known signal/spectrum support. Interestingly, while CS requires sampling to be "randomized" to obtain incoherent artifacts which is critical for reconstruction, OLS mandates sampling to be on a structured lattice. In this paper, we proposed a method to integrate CS with OLS so that both the sparsity and support constraints can be used simultaneously. The method randomizes the sampling on the lattice and minimizes a convex cost function with sparsity constraint and data fidelity terms. Computer simulations in 3D MRI show that the proposed method allows greater accelerations with minimal degradation of the image quality.

A flow quantification method using fluid dynamics regularization and MR tagging.

This paper presents a new method for improved flow analysis and quantification using MRI. The method incorporates fluid dynamics to regularize the flow quantification from tagged MR images. Specifically, the flow quantification is formulated as a minimization problem based on the following: 1) the Navier-Stokes equation governing the fluid dynamics; 2) the flow continuity equation and boundary conditions; and 3) the data consistency constraint. The minimization is carried out using a genetic algorithm. This method is tested using both computer simulations and MR flow experiments. The results are evaluated using flow vector fields from the computational fluid dynamics software package as a reference, which show that the new method can achieve more realistic and accurate flow quantifications than the conventional method.

Minimal-SAR RF pulse optimization for parallel transmission in MRI.

Parallel transmission is an emerging technique to achieve multi-dimensional spatially selective or modulated excitation in Magnetic Resonance Imaging (MRI). Minimizing Specific Absorption Ratio (SAR) is a critical issue in this technique for radio frequency power absorption safety. In this paper, we presented an automatic design method to minimize SAR in an optimization framework. The RF pulses and corresponding k-space trajectory are iteratively adjusted. The method is verified using computer simulations of a 4-channel parallel transmission system. The results showed significantly reduction in SAR can be achieved while the quality of the excited pattern is well preserved without enlonging the pulse duration.

Compressed sensing parallel magnetic resonance imaging.

Both parallel Magnetic Resonance Imaging (pMRI) and Compressed Sensing (CS) can significantly reduce imaging time in MRI, the former by utilizing multiple channel receivers and the latter by utilizing the sparsity of MR images in a transformed domain. In this work, pMRI and CS are integrated to take advantages of the sensitivity information from multiple coils and sparsity characteristics of MR images. Specifically, CS is used as a regularization method for the inverse problem raised by pMRI based on the L1 norm and a Total Variation (TV) term. We test the new method with a set of 8-channel, in-vivo brain MRI data at reduction factors from 2 to 8. Reconstruction results show that the proposed method outperforms several other regularized parallel MRI reconstruction such as the truncated Singular Value Decomposition (SVD) and Tikhonov regularization methods, in terms of residual artifacts and SNR, especially at reduction factors larger than 4.

Single-point Dixon water-fat imaging using 64-channel single-echo acquisition MRI.

This paper presents a method for high-speed water-fat imaging using Single-Echo Acquisition (SEA) with an array of 64 localized coil elements and single-point Dixon sequence. The method forms two-dimensional separate water and fat images from a single echo data. Specifically, a channel correlation and region-growing algorithm was developed to extract the phase information from the single echo data, eliminating the need for multiple data acquisition normally required for water/fat separation. Phantom studies on a 4.7 T scanner show that the method can handle large inter-channel and cross-channel phase variations, even at relative high data noise levels. Assume that the water and fat are spatially separated and they can be identified by the phase discontinuity caused by the chemical frequency shift, the new method can acquire separate water and fat images without reducing the high frame rates of the SEA imaging method. Although its capability is limited if there are large susceptibility artifacts, disconnected tissues, or pixels with mixed fat and water signals, the new method is potentially useful for dynamic imaging of small animals, where the SEA imaging can provide high imaging speed but may suffer from reduced contrast due to the strong fat signals at short repetition time.