Representing diffusion MRI in 5D for segmentation of white matter tracts with a level set method.

Details

Serval ID
serval:BIB_345043CD9745
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Representing diffusion MRI in 5D for segmentation of white matter tracts with a level set method.
Journal
Information Processing in Medical Imaging
Author(s)
Jonasson L., Hagmann P., Bresson X., Thiran J.P., Van Wedeen J.
ISSN
1011-2499 (Print)
ISSN-L
1011-2499
Publication state
Published
Issued date
2005
Volume
19
Pages
311-320
Language
english
Notes
Publication types: Evaluation Studies ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov'tPublication Status: ppublish
Abstract
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.
Keywords
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
Pubmed
Create date
31/01/2012 16:51
Last modification date
20/08/2019 13:20
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