Abstract
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) |
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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 |
Publisher | Springer Verlag |
Pages | 602-610 |
Number of pages | 9 |
ISBN (Print) | 9783319661810 |
DOIs | |
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 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10433 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 |
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Country/Territory | Canada |
City | Quebec City |
Period | 9/11/17 → 9/13/17 |
Bibliographical note
Publisher Copyright:© 2017, Springer International Publishing AG.
Keywords
- Diffusion MRI
- Linear un-mixing
- Multi-shell
- Sparse Bayesian Learning
- Sparse signal recovery