We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm. A localized learning of hyperparameters at each voxel and for each possible fiber orientations improves the parameter estimation. Our experiments using synthetic data from the ISBI 2012 HARDI reconstruction challenge and in-vivo data from the Human Connectome Project demonstrate the improvements.
|Original language||English (US)|
|Title of host publication||Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings|
|Editors||Maxime Descoteaux, Simon Duchesne, Alfred Franz, Pierre Jannin, D. Louis Collins, Lena Maier-Hein|
|Number of pages||9|
|State||Published - 2017|
|Event||20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada|
Duration: Sep 11 2017 → Sep 13 2017
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017|
|Period||9/11/17 → 9/13/17|
Bibliographical noteFunding Information:
Acknowledgemens. This work was partly supported by NIH grants P41 EB015894, P30 NS076408, and the Human Connectome Project (U54 MH091657).
© 2017, Springer International Publishing AG.
- Diffusion MRI
- Linear un-mixing
- Sparse Bayesian Learning
- Sparse signal recovery