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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
EditorsMaxime Descoteaux, Simon Duchesne, Alfred Franz, Pierre Jannin, D. Louis Collins, Lena Maier-Hein
PublisherSpringer- Verlag
Pages602-610
Number of pages9
ISBN (Print)9783319661810
DOIs
StatePublished - Jan 1 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: Sep 11 2017Sep 13 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10433 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period9/11/179/13/17

Fingerprint

Bayesian Learning
Magnetic resonance imaging
Parameter estimation
Learning algorithms
Parameter Estimation
Learning Algorithm
Shell
Glossaries
Unknown Parameters
Fiber
Fiber Orientation
Hyperparameters
Fibers
Gamma distribution
Continuous Distributions
Voxel
Diffusivity
Synthetic Data
Fiber reinforced materials
Volume Fraction

Keywords

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

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

A sparse bayesian learning algorithm for white matter parameter estimation from compressed multi-shell diffusion MRI. / Pisharady, Pramod K; Sotiropoulos, Stamatios N.; Sapiro, Guillermo; Lenglet, Christophe.

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 proceedingConference contribution

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
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)). https://doi.org/10.1007/978-3-319-66182-7_69
Pisharady, Pramod K ; Sotiropoulos, Stamatios N. ; Sapiro, Guillermo ; Lenglet, Christophe. / 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)).
@inproceedings{b1ae0c1bb82b4ba1b74581784adffd4c,
title = "A sparse bayesian learning algorithm for white matter parameter estimation from compressed multi-shell diffusion MRI",
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 K} and Sotiropoulos, {Stamatios N.} and Guillermo Sapiro and Christophe Lenglet",
year = "2017",
month = "1",
day = "1",
doi = "10.1007/978-3-319-66182-7_69",
language = "English (US)",
isbn = "9783319661810",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer- Verlag",
pages = "602--610",
editor = "Maxime Descoteaux and Simon Duchesne and Alfred Franz and Pierre Jannin and Collins, {D. Louis} and Lena Maier-Hein",
booktitle = "Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings",

}

TY - GEN

T1 - A sparse bayesian learning algorithm for white matter parameter estimation from compressed multi-shell diffusion MRI

AU - Pisharady, Pramod K

AU - Sotiropoulos, Stamatios N.

AU - Sapiro, Guillermo

AU - Lenglet, Christophe

PY - 2017/1/1

Y1 - 2017/1/1

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.

KW - Diffusion MRI

KW - Linear un-mixing

KW - Multi-shell

KW - Sparse Bayesian Learning

KW - Sparse signal recovery

UR - http://www.scopus.com/inward/record.url?scp=85029386351&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85029386351&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-66182-7_69

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

M3 - Conference contribution

SN - 9783319661810

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

SP - 602

EP - 610

BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings

A2 - Descoteaux, Maxime

A2 - Duchesne, Simon

A2 - Franz, Alfred

A2 - Jannin, Pierre

A2 - Collins, D. Louis

A2 - Maier-Hein, Lena

PB - Springer- Verlag

ER -