A 3T MR Imaging Investigation of the Topography of Whole Spinal Cord Atrophy in Multiple Sclerosis

Background and Purpose: Spinal cord atrophy is a common feature of MS. However, it is unknown which cord levels are most susceptible to atrophy. We performed whole cord imaging to identify the levels most susceptible to atrophy in patients with MS versus controls and also tested for differences among MS clinical phenotypes. Materials and Methods: Thirty-five patients with MS (2 with CIS, 27 with RRMS, 2 with SPMS, and 4 with PPMS phenotypes) and 27 healthy controls underwent whole cord 3T MR imaging. The spinal cord contour was segmented and assigned to bins representing each C1 to T12 vertebral level. Volumes were normalized, and group comparisons were age-adjusted. Results: There was a trend toward decreased spinal cord volume at the upper cervical levels in PPMS/SPMS versus controls. A trend toward increased spinal cord volume throughout the cervical and thoracic cord in RRMS/CIS versus controls reached statistical significance at the T10 vertebral level. A statistically significant decrease was found in spinal cord volume at the upper cervical levels in PPMS/SPMS versus RRMS/CIS. Conclusions: Opposing pathologic factors impact spinal cord volume measures in MS. Patients with PPMS demonstrated a trend toward upper cervical cord atrophy. However patients with RRMS showed a trend toward increased volume at the cervical and thoracic levels, which most likely reflects inflammation or edema-related cord expansion. With the disease causing both expansion and contraction of the cord, the specificity of spinal cord volume measures for neuroprotective therapeutic effect may be limited

Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts

A novel framework for joint clustering and point-by-point mapping of white matter fiber pathways is presented. Accurate clustering of the trajectories into fiber bundles requires point correspondence along the fiber pathways determined. This knowledge is also crucial for any tract-oriented quantitative analysis. We employ an expectationmaximization (EM) algorithm to cluster the trajectories in a Gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, and point correspondences. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. Probabilistic assignment of the trajectories to clusters is controlled by imposing a minimum threshold on the membership probabilities, to remove outliers in a principled way. The presented results confirm the efficiency and effectiveness of the proposed framework for quantitative analysis of diffusion tensor MRI

Shape-Adaptive DCT for Denoising of 3D Scalar and Tensor Valued Images

During the last ten years or so, diffusion tensor imaging has been used in both research and clinical medical applications. To construct the diffusion tensor images, a large set of direction sensitive magnetic resonance image (MRI) acquisitions are required. These acquisitions in general have a lower signal-to-noise ratio than conventional MRI acquisitions. In this paper, we discuss computationally effective algorithms for noise removal for diffusion tensor magnetic resonance imaging (DTI) using the framework of 3-dimensional shape-adaptive discrete cosine transform. We use local polynomial approximations for the selection of homogeneous regions in the DTI data. These regions are transformed to the frequency domain by a modified discrete cosine transform. In the frequency domain, the noise is removed by thresholding. We perform numerical experiments on 3D synthetical MRI and DTI data and real 3D DTI brain data from a healthy volunteer. The experiments indicate good performance compared to current state-of-the-art methods. The proposed method is well suited for parallelization and could thus dramatically improve the computation speed of denoising schemes for large scale 3D MRI and DTI

In vivo magnetic resonance imaging of acute brain inflammation using microparticles of iron oxide.

Multiple sclerosis is a disease of the central nervous system that is associated with leukocyte recruitment and subsequent inflammation, demyelination and axonal loss. Endothelial vascular cell adhesion molecule-1 (VCAM-1) and its ligand, alpha4beta1 integrin, are key mediators of leukocyte recruitment, and selective inhibitors that bind to the alpha4 subunit of alpha4beta1 substantially reduce clinical relapse in multiple sclerosis. Urgently needed is a molecular imaging technique to accelerate diagnosis, to quantify disease activity and to guide specific therapy. Here we report in vivo detection of VCAM-1 in acute brain inflammation, by magnetic resonance imaging in a mouse model, at a time when pathology is otherwise undetectable. Antibody-conjugated microparticles carrying a large amount of iron oxide provide potent, quantifiable contrast effects that delineate the architecture of activated cerebral blood vessels. Their rapid clearance from blood results in minimal background contrast. This technology is adaptable to monitor the expression of endovascular molecules in vivo in various pathologies