A sparse bayesian learning algorithm for white matter parameter estimation from compressed multi-shell diffusion MRI

Pramod Kumar Pisharady; Stamatios N. Sotiropoulos; Guillermo Sapiro; Christophe Lenglet

doi:10.1007/978-3-319-66182-7_69

A sparse bayesian learning algorithm for white matter parameter estimation from compressed multi-shell diffusion MRI

Pramod Kumar Pisharady, Stamatios N. Sotiropoulos, Guillermo Sapiro, Christophe Lenglet

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

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)
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	https://doi.org/10.1007/978-3-319-66182-7_69
State	Published - 2017
Externally published	Yes
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)
Volume	10433 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Country/Territory	Canada
City	Quebec City
Period	9/11/17 → 9/13/17

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Keywords

Diffusion MRI
Linear un-mixing
Multi-shell
Sparse Bayesian Learning
Sparse signal recovery

Access to Document

10.1007/978-3-319-66182-7_69

Cite this

Pisharady, P. K., Sotiropoulos, S. N., Sapiro, G., & Lenglet, C. (2017). A sparse bayesian learning algorithm for white matter parameter estimation from compressed multi-shell diffusion MRI. In M. Descoteaux, S. Duchesne, A. Franz, P. Jannin, D. L. Collins, & L. Maier-Hein (Eds.), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (pp. 602-610). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10433 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66182-7_69

Pisharady, Pramod Kumar ; Sotiropoulos, Stamatios N. ; Sapiro, Guillermo et al. / A sparse bayesian learning algorithm for white matter parameter estimation from compressed multi-shell diffusion MRI. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. editor / Maxime Descoteaux ; Simon Duchesne ; Alfred Franz ; Pierre Jannin ; D. Louis Collins ; Lena Maier-Hein. Springer Verlag, 2017. pp. 602-610 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

keywords = "Diffusion MRI, Linear un-mixing, Multi-shell, Sparse Bayesian Learning, Sparse signal recovery",

author = "Pisharady, {Pramod Kumar} and Sotiropoulos, {Stamatios N.} and Guillermo Sapiro and Christophe Lenglet",

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Pisharady, PK, Sotiropoulos, SN, Sapiro, G & Lenglet, C 2017, A sparse bayesian learning algorithm for white matter parameter estimation from compressed multi-shell diffusion MRI. in M Descoteaux, S Duchesne, A Franz, P Jannin, DL Collins & L Maier-Hein (eds), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10433 LNCS, Springer Verlag, pp. 602-610, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, Canada, 9/11/17. https://doi.org/10.1007/978-3-319-66182-7_69

A sparse bayesian learning algorithm for white matter parameter estimation from compressed multi-shell diffusion MRI. / Pisharady, Pramod Kumar; Sotiropoulos, Stamatios N.; Sapiro, Guillermo et al.
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. ed. / Maxime Descoteaux; Simon Duchesne; Alfred Franz; Pierre Jannin; D. Louis Collins; Lena Maier-Hein. Springer Verlag, 2017. p. 602-610 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10433 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Sapiro, Guillermo

AU - Lenglet, Christophe

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N2 - 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.

AB - 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.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Pisharady PK, Sotiropoulos SN, Sapiro G, Lenglet C. A sparse bayesian learning algorithm for white matter parameter estimation from compressed multi-shell diffusion MRI. In Descoteaux M, Duchesne S, Franz A, Jannin P, Collins DL, Maier-Hein L, editors, Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Springer Verlag. 2017. p. 602-610. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-66182-7_69