Article: article from journal or magazin.
Representing diffusion MRI in 5D for segmentation of white matter tracts with a level set method.
Information Processing in Medical Imaging
Publication types: Evaluation Studies ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov'tPublication Status: ppublish
We present a method for segmenting white matter tracts from high angular resolution diffusion MR. images by representing the data in a 5 dimensional space of position and orientation. Whereas crossing fiber tracts cannot be separated in 3D position space, they clearly disentangle in 5D position-orientation space. The segmentation is done using a 5D level set method applied to hyper-surfaces evolving in 5D position-orientation space. In this paper we present a methodology for constructing the position-orientation space. We then show how to implement the standard level set method in such a non-Euclidean high dimensional space. The level set theory is basically defined for N-dimensions but there are several practical implementation details to consider, such as mean curvature. Finally, we will show results from a synthetic model and a few preliminary results on real data of a human brain acquired by high angular resolution diffusion MRI.
Algorithms, Artificial Intelligence, Brain/cytology, 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
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