Article: article from journal or magazin.
Representing diffusion MRI in 5-D simplifies regularization and segmentation of white matter tracts.
IEEE Transactions on Medical Imaging
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't Publication Status: ppublish
We present a new five-dimensional (5-D) space representation of diffusion magnetic resonance imaging (dMRI) of high angular resolution. This 5-D space is basically a non-Euclidean space of position and orientation in which crossing fiber tracts can be clearly disentangled, that cannot be separated in three-dimensional position space. This new representation provides many possibilities for processing and analysis since classical methods for scalar images can be extended to higher dimensions even if the spaces are not Euclidean. In this paper, we show examples of how regularization and segmentation of dMRI is simplified with this new representation. The regularization is used with the purpose of denoising and but also to facilitate the segmentation task by using several scales, each scale representing a different level of resolution. We implement in five dimensions the Chan-Vese method combined with active contours without edges for the segmentation and the total variation functional for the regularization. The purpose of this paper is to explore the possibility of segmenting white matter structures directly as entirely separated bundles in this 5-D space. We will present results from a synthetic model and results on real data of a human brain acquired with diffusion spectrum magnetic resonance imaging (MRI), one of the dMRI of high angular resolution available. These results will lead us to the conclusion that this new high-dimensional representation indeed simplifies the problem of segmentation and regularization.
Algorithms, Artificial Intelligence, Brain/anatomy & histology, Diffusion Magnetic Resonance Imaging/methods, Humans, Image Enhancement/methods, Image Interpretation, Computer-Assisted/methods, Imaging, Three-Dimensional/methods, Nerve Fibers, Myelinated/ultrastructure, Pattern Recognition, Automated/methods, Reproducibility of Results, Sensitivity and Specificity
Web of science
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