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.
authors.affiliation: Signal Processing Institute (ITS), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland.
authors.forename: Lisa
authors.initials: L
authors.lastname: Jonasson
authors.forename: Patric
authors.initials: P
authors.lastname: Hagmann
authors.forename: Xavier
authors.initials: X
authors.lastname: Bresson
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Thiran
authors.forename: J
authors.initials: J
authors.lastname: Van Wedeen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000166:cytology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity