Bayesian inference of intravoxel structure in diffusion MRI

While diffusion tensor imaging (DTI) provides a powerful tool to reconstruct neural pathways in vivo, the standard diffusion tensor model is limited to resolve a single fiber direction within each voxel. To overcome this difficulty, high angular resolution diffusion imaging (HARDI) has recently been...

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Bibliographic Details
Main Authors: Haifang Ge, Fitzgerald, William J., Green, Hadrian A. L.
Format: Conference Proceeding
Language:English
Subjects:
Bayes methods
Bayesian analysis
Diffusion
Diffusion tensor imaging
Estimation
Fibers
Heterogeneity
Image resolution
Imaging
Inference
Mathematical analysis
Tensile stress
Tensors
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Summary:While diffusion tensor imaging (DTI) provides a powerful tool to reconstruct neural pathways in vivo, the standard diffusion tensor model is limited to resolve a single fiber direction within each voxel. To overcome this difficulty, high angular resolution diffusion imaging (HARDI) has recently been proposed to investigate intravoxel fiber heterogeneity. In this paper we propose a novel method for mixture model decomposition of the HARDI signal based on Bayesian inference and trans-dimensional Markov Chain simulation. The method is applied to both synthetic and real data.