Computing White Matter Fiber Orientations in High Angular Resolution Diffusion-Weighted MRI

2007 ◽  
Author(s):  
Ning Cao ◽  
Qi Zhuang ◽  
Xuwei Liang ◽  
Ruiwang Huang ◽  
Jun Zhang
Keyword(s):  
White Matter ◽  
Angular Resolution ◽  
High Angular Resolution ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted

Impact of Aging and Sex on Advanced Diffusion-Weighted MRI Measures of White Matter Microstructure

2021 ◽  
Vol 89 (9) ◽  
pp. S184-S185
Author(s):  
Katherine Lawrence ◽  
Leila Nabulsi ◽  
Vigneshwaran Santhalingam ◽  
Zvart Abaryan ◽  
Julio Villalon-Reina ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted ◽  
White Matter Microstructure

scholarly journals Computational Representation of White Matter Fiber Orientations

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Adelino R. Ferreira da Silva
Keyword(s):  
White Matter ◽  
Angular Resolution ◽  
Diffusion Imaging ◽  
Real Data ◽  
Orientation Distribution ◽  
Directional Data ◽  
High Angular Resolution ◽  
Clustering Data ◽  
Von Mises ◽  
Computational Representation

We present a new methodology based on directional data clustering to represent white matter fiber orientations in magnetic resonance analyses for high angular resolution diffusion imaging. A probabilistic methodology is proposed for estimating intravoxel principal fiber directions, based on clustering directional data arising from orientation distribution function (ODF) profiles. ODF reconstructions are used to estimate intravoxel fiber directions using mixtures of von Mises-Fisher distributions. The method focuses on clustering data on the unit sphere, where complexity arises from representing ODF profiles as directional data. The proposed method is validated on synthetic simulations, as well as on a real data experiment. Based on experiments, we show that by clustering profile data using mixtures of von Mises-Fisher distributions it is possible to estimate multiple fiber configurations in a more robust manner than currently used approaches, without recourse to regularization or sharpening procedures. The method holds promise to support robust tractographic methodologies and to build realistic models of white matter tracts in the human brain.

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