Fiber type characterization in skeletal muscle by diffusion tensor imaging

Fiber type distribution within a skeletal muscle, i.e. the quantification of the relative amount of type 1 (slow‐twitching) and type 2 (fast‐twitching) muscle fibers, is of great interest for the monitoring of the effects of training or the treatment of muscle diseases. The purpose of this study was to determine the feasibility of diffusion tensor imaging (DTI) as a tool for noninvasive fiber type quantification in human skeletal muscle. The right calves of 12 healthy volunteers were examined using DTI at 1.5 T. Standard DTI parameters, including fractional anisotropy (FA), and mean, radial and parallel diffusivity (MD, RD and PD, respectively), were determined in the soleus muscle. Fiber type proportion and mean fiber diameter within the soleus muscle were quantified from tissue specimens obtained via a fine needle biopsy. Linear regression analysis tested for associations between DTI and biopsy results. FA values were correlated significantly with fiber type proportion, such that higher FA values indicated a higher proportion of type 1 fibers (R² = 0.5, p = 0.01). This was based on lower diffusivity perpendicular to the main axis of the fiber in subjects with a higher type 1 fiber proportion (RD: R² = 0.52, p = 0.008). MD was also correlated with the proportion of type 1 fibers (R² = 0.37, p = 0.037), whereas PD showed no significant correlation. DTI is a promising method for the noninvasive estimation of fiber type proportion in skeletal muscle. This technique may be used to monitor training effects or may be further developed as a biomarker in certain muscle diseases. Copyright © 2013 John Wiley & Sons, Ltd.     

Longitudinal diffusion tensor imaging in a rat brain glioma model

In order to investigate the properties of water motion within and around brain tumors as a function of tumor growth, longitudinal diffusion tensor imaging (DTI) was carried out in a rat brain glioma (C6) model. As tumors grew in size, significant anisotropy of water diffusion was seen both within and around the tumor. The tissue water surrounding the tumor exhibited high planar anisotropy, as opposed to the linear anisotropy normally seen in white matter, indicating that cells were experiencing stress in a direction normal to the tumor border. When tumors were sufficiently large, significant anisotropy was also seen within the tumor because of longer-range organization of cancer cells within the tumor borders. These findings have important implications for diffusion-weighted MRI experiments examining tumor growth and response to therapy. Copyright (c) 2008 John Wiley & Sons, Ltd.     

弥散张量磁共振成像的新进展

弥散张量磁共振成像技术是近年来出现的一项新技术,由于其对脑白质纤维具有高度敏感性以致在临床上的应用日益广泛,并为人体中枢神经系统的深入研究提供了有效的工具。     

Prognosis of cervical myelopathy based on diffusion tensor imaging with artificial intelligence methods

Diffusion tensor imaging (DTI) has been proposed for the prognosis of cervical myelopathy (CM), but the manual analysis of DTI features is complicated and time consuming. This study evaluated the potential of artificial intelligence (AI) methods in the analysis of DTI for the prognosis of CM. Seventy‐five patients who underwent surgical treatment for CM were recruited for DTI imaging and were divided into two groups based on their one‐year follow‐up recovery. The DTI features of fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity were extracted from DTI maps of all cervical levels. Conventional AI models using logistic regression (LR), k‐nearest neighbors (KNN), and a radial basis function kernel support vector machine (RBF‐SVM) were built using these DTI features. In addition, a deep learning model was applied to the DTI maps. Their performances were compared using 50 repeated 10‐fold cross‐validations. The accuracy of the classifications reached 74.2% ± 1.6% for LR, 85.6% ± 1.4% for KNN, 89.7% ± 1.6% for RBF‐SVM, and 59.2% ± 3.8% for the deep leaning model. The RBF‐SVM algorithm achieved the best accuracy, with sensitivity and specificity of 85.0% ± 3.4% and 92.4% ± 1.9% respectively. This finding indicates that AI methods are feasible and effective for DTI analysis for the prognosis of CM.     

Spatiotemporal microstructural white matter changes in diffusion tensor imaging after transient focal ischemic stroke in rats

Structural reorganization in white matter (WM) after stroke is a potential contributor to substitute or to newly establish the functional field on the injured brain in nature. Diffusion tensor imaging (DTI) is an imaging modality that can be used to evaluate damage and recovery within the brain. This method of imaging allows for in vivo assessment of the restricted movements of water molecules in WM and provides a detailed look at structural connectivity in the brain. For longitudinal DTI studies after a stroke, the conventional region of interest method and voxel‐based analysis are highly dependent on the user‐hypothesis and parameter settings for implementation. In contrast, tract‐based spatial statistics (TBSS) allows for reliable voxel‐wise analysis via the projection of diffusion‐derived parameters onto an alignment‐invariant WM skeleton. In this study, spatiotemporal WM changes were examined with DTI‐derived parameters (fractional anisotropy, FA; mean diffusivity, MD; axial diffusivity, DA; radial diffusivity, RD) using TBSS 2 h to 6 weeks after experimental focal ischemic stroke in rats (N = 6). FA values remained unchanged 2–4 h after the stroke, followed by a continuous decrease in the ipsilesional hemisphere from 24 h to 2 weeks post‐stroke and gradual recovery from the ipsilesional corpus callosum to the external capsule until 6 weeks post‐stroke. In particular, the fibers in these areas were extended toward the striatum of the ischemic boundary region at 6 weeks on tractography. The alterations of the other parameters in the ipsilesional hemisphere showed patterns of a decrease at the early stage, a subsequent pseudo‐normalization of MD and DA, a rapid reduction of RD, and a progressive increase in MD, DA and RD with a decreased extent in the injured area at later stages. The findings of this study may reflect the ongoing processes on tissue damage and spontaneous recovery after stroke.     

Fast and accurate initialization of the free‐water imaging model parameters from multi‐shell diffusion MRI

Cerebrospinal fluid partial volume effect is a known bias in the estimation of Diffusion Tensor Imaging (DTI) parameters from diffusion MRI data. The Free‐Water Imaging model for diffusion MRI data adds a second compartment to the DTI model, which explicitly accounts for the signal contribution of extracellular free‐water, such as cerebrospinal fluid. As a result the DTI parameters obtained through the free‐water model are corrected for partial volume effects, and thus better represent tissue microstructure. In addition, the model estimates the fractional volume of free‐water, and can be used to monitor changes in the extracellular space. Under certain assumptions, the model can be estimated from single‐shell diffusion MRI data. However, by using data from multi‐shell diffusion acquisitions, these assumptions can be relaxed, and the fit becomes more robust. Nevertheless, fitting the model to multi‐shell data requires high computational cost, with a non‐linear iterative minimization, which has to be initialized close enough to the global minimum to avoid local minima and to robustly estimate the model parameters. Here we investigate the properties of the main initialization approaches that are currently being used, and suggest new fast approaches to improve the initial estimates of the model parameters. We show that our proposed approaches provide a fast and accurate initial approximation of the model parameters, which is very close to the final solution. We demonstrate that the proposed initializations improve the final outcome of non‐linear model fitting.     

Characterization of trabecular bone density with ultra‐short echo‐time MRI at 1.5, 3.0 and 7.0 T – comparison with micro‐computed tomography

The goal of this study was to test the potential of ultra‐short echo‐time (UTE) MRI at 1.5, 3.0 and 7.0 T for depiction of trabecular bone structure (of the wrist bones), to evaluate whether T₂* relaxation times of bone water and parametric maps of T₂* of trabecular bone could be obtained at all three field strengths, and to compare the T₂* relaxation times with structural parameters obtained from micro‐computed tomography (micro‐CT) as a reference standard. Ex vivo carpal bones of six wrists were excised en bloc and underwent MRI at 1.5, 3.0 and 7.0 T in a whole‐body MR imager using the head coil. A three‐dimensional radial fat‐suppressed UTE sequence was applied with subsequent acquisitions, with six different echo times T_E of 150, 300, 600, 1200, 3500 and 7000 µs. The T₂* relaxation time and pixel‐wise computed T₂* parametric maps were compared with a micro‐computed‐tomography reference standard providing trabecular bone structural parameters including porosity (defined as the bone‐free fraction within a region of interest), trabecular thickness, trabecular separation, trabecular number and fractal dimension (D_k). T₂* relaxation curves and parametric maps could be computed from datasets acquired at all field strengths. Mean T₂* relaxation times of trabecular bone were 4580 ± 1040 µs at 1.5 T, 2420 ± 560 µs at 3.0 T and 1220 ± 300 µs at 7.0 T, when averaged over all carpal bones. A positive correlation of T₂* with trabecular bone porosity and trabecular separation, and a negative correlation of T₂* relaxation time with trabecular thickness, trabecular number and fractal dimension, was detected (p < 0.01 for all field strengths and micro‐CT parameters). We conclude that UTE MRI may be useful to characterize the structure of trabecular bone, comparable to micro‐CT. Copyright © 2014 John Wiley & Sons, Ltd.     

Considerations in high-resolution skeletal muscle diffusion tensor imaging using single-shot echo planar imaging with stimulated-echo preparation and sensitivity encoding

Previous studies have shown that skeletal muscle diffusion tensor imaging (DTI) can noninvasively probe changes in the muscle fiber architecture and microstructure in diseased and damaged muscles. However, DTI fiber reconstruction in small muscles and in muscle regions close to aponeuroses and tendons remains challenging because of partial volume effects. Increasing the spatial resolution of skeletal muscle single-shot diffusion-weighted echo planar imaging (DW-EPI) can be hindered by the inherently low signal-to-noise ratio (SNR) of muscle DW-EPI because of the short muscle T(2) and the high sensitivity of single-shot EPI to off-resonance effects and T(2)* blurring. In this article, eddy current-compensated diffusion-weighted stimulated-echo preparation is combined with sensitivity encoding (SENSE) to maintain good SNR properties and to reduce the sensitivity to distortions and T(2)* blurring in high-resolution skeletal muscle single-shot DW-EPI. An analytical framework is developed to optimize the reduction factor and diffusion weighting time to achieve maximum SNR. Arguments for the selection of the experimental parameters are then presented considering the compromise between SNR, B(0)-induced distortions, T(2)* blurring effects and tissue incoherent motion effects. On the basis of the selected parameters in a high-resolution skeletal muscle single-shot DW-EPI protocol, imaging protocols at lower acquisition matrix sizes are defined with matched bandwidth in the phase-encoding direction and SNR. In vivo results show that high-resolution skeletal muscle DTI with minimized sensitivity to geometric distortions and T(2)* blurring is feasible using the proposed methodology. In particular, a significant benefit is demonstrated from a reduction in partial volume effects for resolving multi-pennate muscles and muscles with small cross-sections in calf muscle DTI.     

A diffusion tensor imaging analysis of gender differences in water diffusivity within human skeletal muscle

The diffusive properties of adjacent muscles at rest were evaluated in male (n = 12) and female (n = 12) subjects using diffusion tensor imaging (DTI). The principle, second and third eigenvalues, trace of the diffusion tensor [Tr(D)], and two anisotropic parameters, ellipsoid eccentricity (e) and fractional anisotropy (FA), of various muscles in the human calf were calculated from the diffusion tensor. Seven muscles were investigated in this study from images acquired of the left calf: the soleus, lateral gastrocnemius, medial gastrocnemius, posterior tibialis, anterior tibialis, extensor digitorum longus and peroneus longus. A mathematical model was also derived that relates the eigenvalues of the diffusion tensor to the muscle fiber volume fraction, which is defined as the volume of muscle fibers within a well-defined arbitrary muscle volume. Females on average had higher eigenvalues and Tr(D) compared with males, with the majority of muscles being statistically different between the sexes. In contrast, males on average had higher e and FA than females, with the large plantar flexors--soleus, lateral gastrocnemius, and medial gastrocnemius--producing statistically different results. The behavior of the mathematical model for variations in fiber volume fraction produced similar trends to those seen when the experimental data were fit to the model. The model predicts that a larger volume fraction of skeletal muscle in males is devoted to fibers than in females, but the true underlying source of the gender discrepancy remains unclear. Although the model does not fully account for other transport processes, it does provide some insight into the limiting factors that affect the diffusion of water in skeletal muscle measured by DTI.     

User‐independent diffusion tensor imaging analysis pipelines in a rat model presenting ventriculomegalia: A comparison study

Automated analysis of diffusion tensor imaging (DTI) data is an appealing way to process large datasets in an unbiased manner. However, automation can sometimes be linked to a lack of interpretability. Two whole‐brain, automated and voxelwise methods exist: voxel‐based analysis (VBA) and tract‐based spatial statistics (TBSS). In VBA, the amount of smoothing has been shown to influence the results. TBSS is free of this step, but a projection procedure is introduced to correct for residual misalignments. This projection assigns the local highest fractional anisotropy (FA) value to the mean FA skeleton, which represents white matter tract centers. For both methods, the normalization procedure has a major impact. These issues are well documented in humans but, to our knowledge, not in rodents. In this study, we assessed the quality of three different registration algorithms (ANTs SyN, DTI‐TK and FNIRT) using study‐specific templates and their impact on automated analysis methods (VBA and TBSS) in a rat pup model of diffuse white matter injury presenting large unilateral deformations. VBA and TBSS results were stable and anatomically coherent across the three pipelines. For VBA, in regions around the large deformations, interpretability was limited because of the increased partial volume effect. With TBSS, two of the three pipelines found a significant decrease in axial diffusivity (AD) at the known injury site. These results demonstrate that automated voxelwise analyses can be used in an animal model with large deformations.     

弥散张量成像在中枢神经系统中的应用

在磁共振成像(MRI)研究中,弥散张量成像(DTI)是最近几年提出并迅速发展的一个研究方向,本文介绍了弥散张量成像的原理以及国际上主要的研究热点,并结合实际介绍了弥散张量成像在中枢神经系统(CNS)中的应用。     

DTI of human skeletal muscle: the effects of diffusion encoding parameters,signal‐to‐noise ratio and T2 on tensor indices and fiber tracts

In this study, we have performed simulations to address the effects of diffusion encoding parameters, signal‐to‐noise ratio (SNR) and T₂ on skeletal muscle diffusion tensor indices and fiber tracts. Where appropriate, simulations were corroborated and validated by in vivo diffusion tensor imaging (DTI) of human skeletal muscle. Specifically, we have addressed: (i) the accuracy and precision of the diffusion parameters and eigenvectors at different SNR levels; (ii) the effects of the diffusion gradient direction encoding scheme; (iii) the optimal b value for diffusion tensor estimation; (iv) the effects of changes in skeletal muscle T₂; and, finally, the influence of SNR on fiber tractography and derived (v) fiber lengths, (vi) pennation angles and (vii) fiber curvatures. We conclude that accurate DTI of skeletal muscle requires an SNR of at least 25, a b value of between 400 and 500 s/mm², and data acquired with at least 12 diffusion gradient directions homogeneously distributed on half a sphere. Furthermore, for DTI studies focusing on skeletal muscle injury or pathology, apparent changes in the diffusion parameters need to be interpreted with great care in view of the confounding effects of T₂, particularly for moderate to low SNR values. Copyright © 2013 John Wiley & Sons, Ltd.     

Deep learning‐based fully automatic segmentation of wrist cartilage in MR images

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch‐based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in 20 multi‐slice MRI datasets acquired with two different coils in 11 subjects (healthy volunteers and patients). The validation included a comparison with the alternative state‐of‐the‐art CNN methods for the segmentation of joints from MR images and the ground‐truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image‐based and PB‐U‐Net networks. Our PB‐CNN also demonstrated a good agreement with manual segmentation (Sørensen–Dice similarity coefficient [DSC] = 0.81) in the representative (central coronal) slices with a large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter‐ and intra‐observer variability of the manual wrist cartilage segmentation (DSC = 0.78‐0.88 and 0.9, respectively). The proposed deep learning‐based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy.     

Feasibility of precise and reliable glucose quantification in human whole blood samples by 1 tesla benchtop NMR

The standard procedure for blood glucose measurements is enzymatic testing. This method is cheap, but requires small samples of open blood with direct contact to the test medium. In principle, NMR provides non‐contact analysis of body fluids, but high‐field spectrometers are expensive and cannot be easily utilized under clinical conditions. Low‐field NMR systems with permanent magnets are becoming increasingly smaller and more affordable. The studies presented here aim at exploring the capabilities of low‐field NMR for measuring glucose concentrations in whole blood. For this purpose, a modern 1 T benchtop NMR spectrometer was used. Challenges arise from broad spectral lines, the glucose peak locations close to the water signal, low SNR and the interference with signals from other blood components. Whole blood as a sample comprises even more boundary conditions: crucial for reliable results are avoiding the separation of plasma and cells by gravitation and reliable reference values. First, the accuracy of glucose levels measured by NMR was tested using aqueous glucose solutions and commercially available bovine plasma. Then, 117 blood samples from oral glucose tolerance testing were measured with minimal preparation by simple pulse‐acquire NMR experiments. The analysis itself is the key to achieve high precision, so several approaches were investigated: peak integration, orthogonal projection to latent structure analysis and support vector machine regression. Correlations between results from the NMR spectra and the routine laboratory automated analyzer revealed an RMSE of 7.90 mg/dL for the best model. 91.5% of the model output lies within the limits of the German Medical Association guidelines, which require the glucose measurement to be within 11% of the reference method. It is concluded that spectral quantification of glucose in whole blood samples by high‐quality NMR spectrometers operating at 1 T is feasible with sufficient accuracy.     

磁共振弥散张量成像在临床诊疗中的应用

磁共振弥散张量成像(diffusion tensor imaging,DTI)作为一种功能磁共振成像(magnetic resonance imaging,MRI),在临床诊疗中发挥了重要作用.本文先介绍了DTI临床应用的各个领域,重点阐述了其在脑部疾病的应用,后指出临床应用中存在的问题,并对其前景进行了展望.     

A meta-analysis of diffusion tensor imaging studies in amyotrophic lateral sclerosis

Studies involving diffusion tensor imaging (DTI) of amyotrophic lateral sclerosis (ALS) with whole-brain voxel-based analysis yielded variable findings. A systematic review was conducted on whole-brain voxel-based diffusion tensor imaging fractional anisotropy (FA) studies of ALS patients and healthy controls (HC) in PubMed, ISI Web of Science, Embase, and MEDLINE databases from 1990 to December 25, 2010. Coordinates were extracted from clusters with significant difference in FA between ALS patients and HC. Meta-analysis was performed using signed differential mapping. Eight studies were enrolled, comprising 143 ALS patients and 145 HC. The included studies reported FA reduction at 67 coordinates in ALS and no FA increased. Significant reductions were present in the bilateral frontal white matter/cingulate gyrus and the posterior limb of bilateral internal capsule. The findings remain largely unchanged in quartile and jackknife sensitivity analyses. Our finding suggests that ALS is a multisystem disease beyond motor dysfunction and provides evidence that FA reduction in the frontal white matter and cingulate gyrus may be a special biomarker of ALS.     

Tract‐specific and age‐related variations of the spinal cord microstructure: a multi‐parametric MRI study using diffusion tensor imaging (DTI) and inhomogeneous magnetization transfer (ihMT)

Being able to finely characterize the spinal cord (SC) microstructure and its alterations is a key point when investigating neural damage mechanisms encountered in different central nervous system (CNS) pathologies, such as multiple sclerosis, amyotrophic lateral sclerosis or myelopathy. Based on novel methods, including inhomogeneous magnetization transfer (ihMT) and dedicated SC probabilistic atlas post‐processing, the present study focuses on the in vivo characterization of the healthy SC tissue in terms of regional microstructure differences between (i) upper and lower cervical vertebral levels and (ii) sensory and motor tracts, as well as differences attributed to normal aging. Forty‐eight healthy volunteers aged from 20 to 70 years old were included in the study and scanned at 3 T using axial high‐resolution T₂*‐w imaging, diffusion tensor imaging (DTI) and ihMT, at two vertebral levels (C2 and C5). A processing pipeline with minimal user intervention, SC segmentation and spatial normalization into a reference space was implemented in order to assess quantitative morphological and structural parameters (cross‐sectional areas, scalar DTI and MT/ihMT metrics) in specific white and gray matter regions of interest. The multi‐parametric MRI metrics collected allowed upper and lower cervical levels to be distinguished, with higher ihMT ratio (ihMTR), higher axial diffusivity (λ_∥) and lower radial diffusivity (λ_⊥) at C2 compared with C5. Significant differences were also observed between white matter fascicles, with higher ihMTR and lower λ_∥ in motor tracts compared with posterior sensory tracts. Finally, aging was found to be associated with significant metric alterations (decreased ihMTR and λ_∥). The methodology proposed here, which can be easily transferred to the clinic, provides new insights for SC characterization. It bears great potential to study focal and diffuse SC damage in neurodegenerative and demyelinating diseases. Copyright © 2016 John Wiley & Sons, Ltd.     

High‐resolution diffusion tensor imaging in cervical spondylotic myelopathy: a preliminary follow‐up study

Diffusion imaging is a promising technique as it can provide microstructural tissue information and thus potentially show viable changes in spinal cord. However, the traditional single‐shot imaging method is limited as a result of various image artifacts. In order to improve measurement accuracy, we used a newly developed, multi‐shot, high‐resolution, diffusion tensor imaging (DTI) method to investigate diffusion metric changes and compare them with T₂‐weighted (T2W) images before and after decompressive surgery for cervical spondylotic myelopathy (CSM). T2W imaging, single‐shot DTI and multi‐shot DTI were employed to scan seven patients with CSM before and 3 months after decompressive surgery. High signal intensities were scored using the T2 W images. DTI metrics, including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD), were quantified and compared pre‐ and post‐surgery. In addition, the relationship between imaging metrics and neurological assessments was examined. The reproducibility of multi‐shot DTI was also assessed in 10 healthy volunteers. Post‐surgery, the mean grade of cervical canal stenosis was reduced from grade 3 to normal after 3 months. Compared with single‐shot DTI, multi‐shot DTI provided better images with lower artifact levels, especially following surgery, as a result of reduced artifacts from metal implants. The new method also showed acceptable reproducibility. Both FA and RD values from the new acquisition showed significant differences post‐surgery (FA, p = 0.026; RD, p = 0.048). These changes were consistent with neurological assessments. In contrast, T2W images did not show significant changes before and after surgery. Multi‐shot diffusion imaging showed improved image quality over single‐shot DWI, and presented superior performance in diagnosis and recovery monitoring for patients with CSM compared with T2W imaging. DTI metrics can reflect the pathological conditions of spondylotic spinal cord quantitatively and may serve as a sensitive biomarker for potential CSM management.     

Validation of diffusion tensor MRI in the central nervous system using light microscopy: quantitative comparison of fiber properties

Diffusion tensor imaging (DTI) provides an indirect measure of tissue structure on a microscopic scale. To date, DTI is the only imaging method that provides such information in vivo, and has proven to be a valuable tool in both research and clinical settings. In this study, we investigated the relationship between white matter structure and diffusion parameters measured by DTI. We used micrographs from light microscopy of fixed, myelin‐stained brain sections as a gold standard for direct comparison with data from DTI. Relationships between microscopic tissue properties observed with light microscopy (fiber orientation, density and coherence) and fiber properties observed by DTI (tensor orientation, diffusivities and fractional anisotropy) were investigated. Agreement between the major eigenvector of the tensor and myelinated fibers was excellent in voxels with high fiber coherence. In addition, increased fiber spread was strongly associated with increased radial diffusivity (p = 6 × 10^–6) and decreased fractional anisotropy (p = 5 × 10^–8), and was weakly associated with decreased axial diffusivity (p = 0.07). Increased fiber density was associated with increased fractional anisotropy (p = 0.03), and weakly associated with decreased radial diffusivity (p < 0.06), but not with axial diffusivity (p = 0.97). The mean diffusivity was largely independent of fiber spread (p = 0.24) and fiber density (p = 0.34). Copyright © 2012 John Wiley & Sons, Ltd.