A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI

  • Pramod Kumar PisharadyEmail author
  • Stamatios N. Sotiropoulos
  • Guillermo Sapiro
  • Christophe Lenglet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)


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.


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



This work was partly supported by NIH grants P41 EB015894, P30 NS076408, and the Human Connectome Project (U54 MH091657).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pramod Kumar Pisharady
    • 1
    Email author
  • Stamatios N. Sotiropoulos
    • 2
    • 3
  • Guillermo Sapiro
    • 4
    • 5
  • Christophe Lenglet
    • 1
  1. 1.CMRR, RadiologyUniversity of MinnesotaMinneapolisUSA
  2. 2.Centre for Functional MRI of the Brain (FMRIB)University of OxfordOxfordUK
  3. 3.School of Medicine, Sir Peter Mansfield Imaging CentreUniversity of NottinghamNottinghamUK
  4. 4.Electrical and Computer EngineeringDuke UniversityDurhamUSA
  5. 5.Biomedical Engineering and Computer ScienceDuke UniversityDurhamUSA

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