Stochastic Algorithms for White Matter Fiber Tracking and the Inference of Brain Connectivity from MR Diffusion Tensor Data

We consider several stochastic algorithms for fiber tracking and compute the connectivity matrix from data obtained by magnetic resonance diffusion tensor imaging of the living human brain

Diffusion Tensor Imaging

This unit provides step‐by‐step instructions on how to perform diffusion tensor imaging (DTI) in a clinical setting. A brief introduction on DTI techniques and current clinical applications is also presented. Additional technical details, practical considerations, and anticipated results are discussed in a commentary section.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145355/1/cpmia0604.pd

Diffusion tensor imaging with direct cytopathological validation: Characterisation of decorin treatment in experimental juvenile communicating hydrocephalus

BACKGROUND: In an effort to develop novel treatments for communicating hydrocephalus, we have shown previously that the transforming growth factor-β antagonist, decorin, inhibits subarachnoid fibrosis mediated ventriculomegaly; however decorin’s ability to prevent cerebral cytopathology in communicating hydrocephalus has not been fully examined. Furthermore, the capacity for diffusion tensor imaging to act as a proxy measure of cerebral pathology in multiple sclerosis and spinal cord injury has recently been demonstrated. However, the use of diffusion tensor imaging to investigate cytopathological changes in communicating hydrocephalus is yet to occur. Hence, this study aimed to determine whether decorin treatment influences alterations in diffusion tensor imaging parameters and cytopathology in experimental communicating hydrocephalus. Moreover, the study also explored whether diffusion tensor imaging parameters correlate with cellular pathology in communicating hydrocephalus. METHODS: Accordingly, communicating hydrocephalus was induced by injecting kaolin into the basal cisterns in 3-week old rats followed immediately by 14 days of continuous intraventricular delivery of either human recombinant decorin (n = 5) or vehicle (n = 6). Four rats remained as intact controls and a further four rats served as kaolin only controls. At 14-days post-kaolin, just prior to sacrifice, routine magnetic resonance imaging and magnetic resonance diffusion tensor imaging was conducted and the mean diffusivity, fractional anisotropy, radial and axial diffusivity of seven cerebral regions were assessed by voxel-based analysis in the corpus callosum, periventricular white matter, caudal internal capsule, CA1 hippocampus, and outer and inner parietal cortex. Myelin integrity, gliosis and aquaporin-4 levels were evaluated by post-mortem immunohistochemistry in the CA3 hippocampus and in the caudal brain of the same cerebral structures analysed by diffusion tensor imaging. RESULTS: Decorin significantly decreased myelin damage in the caudal internal capsule and prevented caudal periventricular white matter oedema and astrogliosis. Furthermore, decorin treatment prevented the increase in caudal periventricular white matter mean diffusivity (p = 0.032) as well as caudal corpus callosum axial diffusivity (p = 0.004) and radial diffusivity (p = 0.034). Furthermore, diffusion tensor imaging parameters correlated primarily with periventricular white matter astrocyte and aquaporin-4 levels. CONCLUSIONS: Overall, these findings suggest that decorin has the therapeutic potential to reduce white matter cytopathology in hydrocephalus. Moreover, diffusion tensor imaging is a useful tool to provide surrogate measures of periventricular white matter pathology in communicating hydrocephalus. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12987-016-0033-2) contains supplementary material, which is available to authorized users

Probing white-matter microstructure with higher-order diffusion tensors and susceptibility tensor MRI.

Diffusion MRI has become an invaluable tool for studying white matter microstructure and brain connectivity. The emergence of quantitative susceptibility mapping and susceptibility tensor imaging (STI) has provided another unique tool for assessing the structure of white matter. In the highly ordered white matter structure, diffusion MRI measures hindered water mobility induced by various tissue and cell membranes, while susceptibility sensitizes to the molecular composition and axonal arrangement. Integrating these two methods may produce new insights into the complex physiology of white matter. In this study, we investigated the relationship between diffusion and magnetic susceptibility in the white matter. Experiments were conducted on phantoms and human brains in vivo. Diffusion properties were quantified with the diffusion tensor model and also with the higher order tensor model based on the cumulant expansion. Frequency shift and susceptibility tensor were measured with quantitative susceptibility mapping and susceptibility tensor imaging. These diffusion and susceptibility quantities were compared and correlated in regions of single fiber bundles and regions of multiple fiber orientations. Relationships were established with similarities and differences identified. It is believed that diffusion MRI and susceptibility MRI provide complementary information of the microstructure of white matter. Together, they allow a more complete assessment of healthy and diseased brains

Data augmentation in Rician noise model and Bayesian Diffusion Tensor Imaging

Mapping white matter tracts is an essential step towards understanding brain function. Diffusion Magnetic Resonance Imaging (dMRI) is the only noninvasive technique which can detect in vivo anisotropies in the 3-dimensional diffusion of water molecules, which correspond to nervous fibers in the living brain. In this process, spectral data from the displacement distribution of water molecules is collected by a magnetic resonance scanner. From the statistical point of view, inverting the Fourier transform from such sparse and noisy spectral measurements leads to a non-linear regression problem. Diffusion tensor imaging (DTI) is the simplest modeling approach postulating a Gaussian displacement distribution at each volume element (voxel). Typically the inference is based on a linearized log-normal regression model that can fit the spectral data at low frequencies. However such approximation fails to fit the high frequency measurements which contain information about the details of the displacement distribution but have a low signal to noise ratio. In this paper, we directly work with the Rice noise model and cover the full range of $b$-values. Using data augmentation to represent the likelihood, we reduce the non-linear regression problem to the framework of generalized linear models. Then we construct a Bayesian hierarchical model in order to perform simultaneously estimation and regularization of the tensor field. Finally the Bayesian paradigm is implemented by using Markov chain Monte Carlo.Comment: 37 pages, 3 figure

Diffusion Tensor Imaging as a novel technique in early detection of cervical spondylotic myelopathy

Introduction: Diffusion tensor imaging (DTI) is an advanced MR imaging technique which helps in early detection of cervical spondylotic myelopathy (CSM). Decompressive surgery performed during early stages of the disease was reported to be more successful when compared with later stages. Aim: To evaluate the usefulness of diffusion tensor imaging (DTI) in early stages of cervical spondylotic myelopathy (CSM) and to aid in better surgical outcome. Materials and methods: This prospective observational study included 25 patients with clinical diagnosis of cervical spondylotic myelopathy who underwent routine MRI of the cervical spine. Conventional MRI sequences along with diffusion tensor imaging (DTI) were performed. Quantitative fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were compared at stenotic and nonstenotic segments. Results: A statistically significant difference in mean FA and ADC values were seen at stenotic and nonstenotic segments. In the most stenotic segments, the mean FA value was 0.415 ± 0.203 and in the nonstenotic segment, the mean FA value was 0.717 ± 0.160, which was statistically significant (P < 0.001). The mean ADC value in the most stenotic segments was 1.777 ± 1.005 x 10-3 mm2/s and that of the nonstenotic segments was 1.010 ± 0.458 x 10-3 mm2/s. The difference in the mean ADC value was statistically significant (p <0.001). Conclusion: Use of diffusion tensor imaging (DTI) along with conventional MRI sequences enables early detection of the disease and helps in appropriate timing of surgery. Keywords: Diffusion tensor imaging (DTI), cervical spondylotic myelopathy (CSM), apparent diffusion coefficient (ADC), fractional anisotropy (FA)

Clinical applications of diffusion tensor tractography of the spinal cord

Diffusion tensor imaging (DTI) can visualize the white matter tracts in vivo. The aim of this study was to assess the clinical utility of DTI in patients with diseases of the spinal cord. Fourteen subjects underwent magnetic resonance imaging of the spine at 1.5T. Preliminary diagnosis of the patients suggested traumatic, tumorous, ischemic or inflammatory lesions of the spinal cord. In addition to T2-weighted images, DTI was performed with the gradients in 30 orthogonal directions. Maps of the apparent diffusion coefficient and of fractional anisotropy were reconstructed. Diffusion tensor imaging showed a clear displacement and deformation of the white matter tracts at the level of the pathological lesions in the spinal cord. This capability of diffusion tensor imaging to reliably display secondary alterations to the white matter tracts caused by the primary lesion has the potential to be of great utility for treatment planning and follow-u

Reduced Myelin Water in the White Matter Tracts of Patients with Niemann-Pick Disease Type C

SUMMARY: Previous studies using diffusion tensor imaging to examine white matter in Niemann-Pick disease type C have produced mixed results. However, diffusion tensor imaging does not directly measure myelin and may be affected by other structural changes. We used myelin water imaging to more directly examine demyelination in 2 patients with Niemann-Pick disease type C. The results suggest that this technique may be useful for identifying regional changes in myelination in this condition