Paper Info
Title
Representing diffusion MRI in 5D for segmentation of white matter tracts with a level set method.
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.
Year
DOI
Venue
2005
10.1007/11505730_26
IPMI
Keywords
Field
DocType
position-orientation space,standard level set method,high angular resolution diffusion,mr image,level set theory,white matter tract,non-euclidean high dimensional space,diffusion mri,dimensional space,level set method,position space,level set,mean curvature
Computer vision,Diffusion MRI,Five-dimensional space,Pattern recognition,Segmentation,Level set method,Computer science,Mean curvature,Level set,Angular resolution,Artificial intelligence,Probability density function
Conference
Volume
ISSN
ISBN
19
1011-2499
3-540-26545-7
Citations 
PageRank 
References 
7
0.65
4
Authors
5
Name
Order
Citations
PageRank
Lisa Jonasson11127.53
P. Hagmann251135.38
Xavier Bresson3184268.08
Jean-Philippe Thiran42320257.56
Van Jay Wedeen518312.20