Representing Diffusion MRI in 5D for Segmentation of White Matter Tracts with a Level Set Method

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 positionorientation 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.

• Type: Conference
• Year: 2005
• Where: IPMI
• Authors: Lisa Jonasson, Patric Hagmann, Xavier Bresson, Jean-Philippe Thiran, Van J. Wedeen
• Keywords: 5D Level, Angular Resolution Diffusion, IPMI 2005, Level Set Method, Medical Imaging