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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Jonasson, L., Hagmann, P., Bresson, X., Thiran, J. P., & Wedeen, V. J. (2005). Representing diffusion MRI in 5D for segmentation of white matter tracts with a level set method. In Lecture Notes in Computer Science (Vol. 3565, pp. 311–320). Springer Verlag. https://doi.org/10.1007/11505730_26
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