Representing diffusion MRI in 5D for segmentation of white matter tracts with a level set method.
Data(s) |
2005
|
---|---|
Resumo |
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. |
Identificador |
http://serval.unil.ch/?id=serval:BIB_345043CD9745 isbn:1011-2499 (Print) pmid:17354705 |
Idioma(s) |
en |
Fonte |
Information Processing in Medical Imaging, vol. 19, pp. 311-320 |
Palavras-Chave | #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 |
Tipo |
info:eu-repo/semantics/article article |