Title
Multi-fiber reconstruction from DW-MRI using a continuous mixture of hyperspherical von Mises-Fisher distributions.
Abstract
Multi-fiber reconstruction has attracted immense attention lately in the field of diffusion weighted MRI analysis. Several mathematical models have been proposed in literature but there is still scope for improvement. The key issues of importance in multi-fiber reconstruction are, fiber detection accuracy, robustness to noise and computational efficiency. To this end, we propose a novel mathematical model for representing the MR signal attenuation in the presence of multiple fibers at a single voxel and estimate the parameters of this model given the diffusion weighted MRI data. Our model for the diffusion MR signal consists of a continuous mixture of Hyperspherical von Mises-Fisher distributions. Being a continuous mixture, our model does not require the specification of the number of mixture components. We present a closed form expression for this continuous mixture that leads to a computationally efficient implementation. To validate our model we present extensive results on both synthetic and real data (human and rat brain) and demonstrate that even in presence of noise, our model clearly outperforms the state-of-the-art methods in fiber orientation estimation while maintaining a substantial computational advantage.
Year
DOI
Venue
2009
10.1007/978-3-642-02498-6_12
IPMI
Keywords
Field
DocType
diffusion weighted mri data,mr signal attenuation,diffusion weighted mri analysis,multi-fiber reconstruction,computational efficiency,novel mathematical model,mathematical model,continuous mixture,diffusion mr signal,hyperspherical von mises-fisher distributions,mixture component
Voxel,Diffusion MRI,Fiber,Pattern recognition,Computer science,Closed-form expression,Robustness (computer science),Artificial intelligence,Attenuation,Mathematical model,von Mises yield criterion
Conference
Volume
ISSN
Citations 
21
1011-2499
1
PageRank 
References 
Authors
0.37
11
6
Name
Order
Citations
PageRank
Ritwik Kumar1868.23
B.C. Vemuri24208536.42
Fei Wang327219.41
Tanveer Syeda-Mahmood418816.00
Paul R Carney527032.09
Thomas H. Mareci639742.15