Assessing Optic Neuritis in a Mouse Model of Multiple Sclerosis with Diffusion MR Imaging

Optic neuritis (ON) is an early manifestation in patients of multiple sclerosis (MS), typically resulting in visual dysfunction. The inflammatory demyelination of the optic nerve in ON closely resembles pathologies of the rest of central nervous system (CNS) white matter in MS. Since accumulated axonal degeneration in MS was considered as the potential cause leading to permanent disability, correlating optic nerve pathology and visual function in ON could be a model system to investigate the relationship between functional outcome and neuropathology. It may also present a new way to reflect the disease progression in MS. Various MR techniques have been used to assess inflammation (inflammatory cell infiltration and vasogenic edema) of ON, but rarely demonstrated the ability to image cellularity changes non-invasively. Diffusion MRI measures the Brownian motion of water molecules in the microstructure of biological tissues. Diffusion tensor imaging (DTI) holds the promise to provide a specific biomarker of axonal injury and demyelination in CNS white matter by axial diffusivity (the diffusion parallel to white matter fibers) and radial diffusivity (the diffusion perpendicular to white matter fibers), respectively. However, DTI assumes a single diffusion tensor model and thus takes an average of varied diffusion components. In contrast, our recently developed diffusion basis spectrum imaging (DBSI) resolves the complex diffusion components and provides relatively accurate directional diffusivities and diffusion component fractions, relating to the detail and accurate pathological picture of the disease or injury. In the current work, in vivo 25-direction DBSI was applied to the optic nerve of mice with experimental autoimmune encephalomyelitis (EAE), an animal model of MS, with visual impairment at onset of ON. Our results demonstrate that inflammation correlated well with visual impairment in acute ON. DBSI successfully detected inflammatory cell infiltration and optic nerve white matter pathology in EAE that was consistent with histology, supporting the capability of DBSI to quantify increased cellularity, axonal injury and myelin damage in the optic nerve of EAE mice

Diffusion Tensor Imaging Based Tractography of Human Brain Fiber Bundles

Tractography is a non-invasive process for reconstruction, modeling and visualization of neural fibers in the white matter (WM) of human brain. It has emerged as a major breakthrough for neuroscience research due to its usefulness in clinical applications. Two types of tractography approaches: deterministic and probabilistic have been investigated to evaluate their performances on tracking fiber bundles using diffusion tensor imaging (DTI). The images are taken by applying pulsed magnetic fields in multiple gradient directions. After removing the non-brain areas from the images, the diffusion tensor indices for each image voxel are calculated. White matter connectivity of the brain, i.e. tractography, is primarily based upon streamline algorithms where the local tract direction is defined by the principle direction of the diffusion tensor. Simulations are performed using three approaches: fiber assignment by continuous tracking (FACT), probability index of connectivity (PICo) and Gibbs tracking (GT). Simulation results show that probabilistic tractography i.e. PICo and GT can reconstruct longer length of fibers compared to the deterministic approach-FACT but with a cost of high computation time. Moreover, GT handles the more complex fiber configurations of crossing and kissing fibers, more effectively and provides the best reconstruction of fibers. In addition, diffusion tensor indices: fractional anisotropy (FA) and mean diffusivity (MD) for a region of interest can be quantified and used to assess several brain diseases. Prospective investigation of DTI based tractography can reveal useful information on WM architecture in normal and diseased brain which will speed up the detection and treatment of various brain diseases

Spatial mapping of translational diffusion coefficients using diffusion tensor imaging: A mathematical description

In this article, we discuss the theoretical background for diffusion weighted imaging and diffusion tensor imaging. Molecular diffusion is a random process involving thermal Brownian motion. In biological tissues, the underlying microstructures restrict the diffusion of water molecules, making diffusion directionally dependent. Water diffusion in tissue is mathematically characterized by the diffusion tensor, the elements of which contain information about the magnitude and direction of diffusion and is a function of the coordinate system. Thus, it is possible to generate contrast in tissue based primarily on diffusion effects. Expressing diffusion in terms of the measured diffusion coefficient (eigenvalue) in any one direction can lead to errors. Nowhere is this more evident than in white matter, due to the preferential orientation of myelin fibers. The directional dependency is removed by diagonalization of the diffusion tensor, which then yields a set of three eigenvalues and eigenvectors, representing the magnitude and direction of the three orthogonal axes of the diffusion ellipsoid, respectively. For example, the eigenvalue corresponding to the eigenvector along the long axis of the fiber corresponds qualitatively to diffusion with least restriction. Determination of the principal values of the diffusion tensor and various anisotropic indices provides structural information. We review the use of diffusion measurements using the modified Stejskal–Tanner diffusion equation. The anisotropy is analyzed by decomposing the diffusion tensor based on symmetrical properties describing the geometry of diffusion tensor. We further describe diffusion tensor properties in visualizing fiber tract organization of the human brain

Time-optimized high-resolution readout-segmented diffusion tensor imaging

Readout-segmented echo planar imaging with 2D navigator-based reacquisition is an uprising technique enabling the sampling of high-resolution diffusion images with reduced susceptibility artifacts. However, low signal from the small voxels and long scan times hamper the clinical applicability. Therefore, we introduce a regularization algorithm based on total variation that is applied directly on the entire diffusion tensor. The spatially varying regularization parameter is determined automatically dependent on spatial variations in signal-to-noise ratio thus, avoiding over- or under-regularization. Information about the noise distribution in the diffusion tensor is extracted from the diffusion weighted images by means of complex independent component analysis. Moreover, the combination of those features enables processing of the diffusion data absolutely user independent. Tractography from in vivo data and from a software phantom demonstrate the advantage of the spatially varying regularization compared to un-regularized data with respect to parameters relevant for fiber-tracking such as Mean Fiber Length, Track Count, Volume and Voxel Count. Specifically, for in vivo data findings suggest that tractography results from the regularized diffusion tensor based on one measurement (16 min) generates results comparable to the un-regularized data with three averages (48 min). This significant reduction in scan time renders high resolution (1×1×2.5 mm3) diffusion tensor imaging of the entire brain applicable in a clinical context

Diffusion Tensor Imaging: Exploring the Motor Networks and Clinical Applications

With the advances in diffusion magnetic resonance (MR) imaging techniques, diffusion tensor imaging (DTI) has been applied to a number of neurological conditions because DTI can demonstrate microstructures of the brain that are not assessable with conventional MR imaging. Tractography based on DTI offers gross visualization of the white matter fiber architecture in the human brain in vivo. Degradation of restrictive barriers and disruption of the cytoarchitecture result in changes in the diffusion of water molecules in various pathological conditions, and these conditions can also be assessed with DTI. Yet many factors may influence the ability to apply DTI clinically, so these techniques have to be used with a cautious hand

Evaluating the accuracy of diffusion MRI models in white matter

Models of diffusion MRI within a voxel are useful for making inferences about the properties of the tissue and inferring fiber orientation distribution used by tractography algorithms. A useful model must fit the data accurately. However, evaluations of model-accuracy of some of the models that are commonly used in analyzing human white matter have not been published before. Here, we evaluate model-accuracy of the two main classes of diffusion MRI models. The diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian distribution. Sparse fascicle models (SFM) summarize the signal as a linear sum of signals originating from a collection of fascicles oriented in different directions. We use cross-validation to assess model-accuracy at different gradient amplitudes (b-values) throughout the white matter. Specifically, we fit each model to all the white matter voxels in one data set and then use the model to predict a second, independent data set. This is the first evaluation of model-accuracy of these models. In most of the white matter the DTM predicts the data more accurately than test-retest reliability; SFM model-accuracy is higher than test-retest reliability and also higher than the DTM, particularly for measurements with (a) a b-value above 1000 in locations containing fiber crossings, and (b) in the regions of the brain surrounding the optic radiations. The SFM also has better parameter-validity: it more accurately estimates the fiber orientation distribution function (fODF) in each voxel, which is useful for fiber tracking

Isotropic non-white matter partial volume effects in constrained spherical deconvolution

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DVV signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple non-parallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNRs), fiber configurations, and tissue fractions. Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35-50% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM G M interface is especially important. We suggest acquiring data with high diffusion-weighting 2500-3000 s/mm(2), reasonable SNR (similar to 30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD

Diffusion tensor imaging of the rotator cuff muscles : optimisation of b value and number of diffusion direction for imaging at 3 Tesla MRI

Background: Diffusion tensor imaging is a standard method for evaluation of musculoskeletal pathology. The objective of this study is to determine the optimal b-value and number of diffusion direction for diffusion tensor imaging of rotator cuff muscles in healthy subjects on 3.0 Tesla MRI. Methodology: 38 healthy volunteers were included in this prospective study. Diffusion tensor imaging (DTI) of the rotator cuff muscles (subscapularis, supraspinatus and infraspinatus muscles) was performed with single-shot spin-echo echo-planar imaging sequences in 16 and 32 diffusion directions. Three different b-values were selected for each diffusion direction: b-value 400 s/mm2, 600s/mm2 and 800s/mm2. Signal to noise ratio (SNR) was measured in each muscle at different b-values and number of diffusion direction, and average SNR were calculated. Statistical analysis was performed with one-way ANOVA and paired t-test. Mean and standard deviation were obtained for each DTI parameters, and level of significance was determined (p < 0.05). Results: The signal to noise ratio was highest for all three muscles at b-value 400s/mm2 (subscapularis, 40.255; supraspinatus, 38.203; and infraspinatus, 48.232) followed by b-value 600s/mm2 and 800s/mm2. The use of 32 number of diffusion directions had shown to improve SNR value compared to 16 diffusion directions (P<0.05). Conclusions: The optimal combination of b value and number of diffusion directions in DTI of rotator cuff muscles on 3.0 Tesla MRI is 400s/mm2 and 32 directions respectively

Doctor of Philosophy

dissertationDiffusion tensor MRI (DT-MRI or DTI) has been proven useful for characterizing biological tissue microstructure, with the majority of DTI studies having been performed previously in the brain. Other studies have shown that changes in DTI parameters are detectable in the presence of cardiac pathology, recovery, and development, and provide insight into the microstructural mechanisms of these processes. However, the technical challenges of implementing cardiac DTI in vivo, including prohibitive scan times inherent to DTI and measuring small-scale diffusion in the beating heart, have limited its widespread usage. This research aims to address these technical challenges by: (1) formulating a model-based reconstruction algorithm to accurately estimate DTI parameters directly from fewer MRI measurements and (2) designing novel diffusion encoding MRI pulse sequences that compensate for the higher-order motion of the beating heart. The model-based reconstruction method was tested on undersampled DTI data and its performance was compared against other state-of-the-art reconstruction algorithms. Model-based reconstruction was shown to produce DTI parameter maps with less blurring and noise and to estimate global DTI parameters more accurately than alternative methods. Through numerical simulations and experimental demonstrations in live rats, higher-order motion compensated diffusion-encoding was shown to successfully eliminate signal loss due to motion, which in turn produced data of sufficient quality to accurately estimate DTI parameters, such as fiber helix angle. Ultimately, the model-based reconstruction and higher-order motion compensation methods were combined to characterize changes in the cardiac microstructure in a rat model with inducible arterial hypertension in order to demonstrate the ability of cardiac DTI to detect pathological changes in living myocardium

Dependence of brain DTI maps of fractional anisotropy and mean diffusivity on the number of diffusion weighting directions

The rotational variance dependence of diffusion tensor imaging (DTI) derived parameters on the number of diffusion weighting directions (N) has been investigated by several Monte Carlo simulation studies. However, the dependence of fractional anisotropy (FA) and mean diffusivity (MD) maps on N, in terms of accuracy and contrast between different anatomical structures, has not been assessed in detail. This experimental study further investigated in vivo the effect of the number of diffusion weighting directions on DTI maps of FA and MD. Human brain FA and MD maps of six healthy subjects were acquired at 1.5T with varying N (6, 11, 19, 27, 55). Then, FA and MD mean values in high (FAH, MDH) and low (FAL, MDL) anisotropy segmented brain regions were measured. Moreover, the contrast-to-signal variance ratio (CVRFA, CVRMD) between the main white matter and the surrounding regions was calculated. Analysis of variance showed that FAL, FAH and CVRFA significantly (p 0.05) depend on N. Unlike MD values, FA values significantly vary with N. It is noteworthy that the observed variation is opposite in low and high anisotropic regions. In clinical studies, the effect of N may represent a confounding variable for anisotropy measurements and the employment of DTI acquisition schemes with high N (> 20) allows an increased CVR and a better visualization of white matter structures in FA maps