Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning

Pramod Kumar Pisharady, Stamatios N. Sotiropoulos, Julio M. Duarte-Carvajalino, Guillermo Sapiro, Christophe Lenglet

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates.

Original languageEnglish (US)
Pages (from-to)488-503
Number of pages16
JournalNeuroImage
Volume167
DOIs
StatePublished - Feb 15 2018

Bibliographical note

Funding Information:
This work was partly supported by NIH grants P41 EB015894 , P30 NS076408 , R01 EB008432 and the Human Connectome Project ( U54 MH091657 ). The work of GS on sparse modeling is also supported by NSF , ARO , ONR , and NGA . We would like to thank Sudhir Ramanna for his help with the acquisition of the in-vivo dataset used in this paper. Appendix A

Publisher Copyright:
© 2017 Elsevier Inc.

Keywords

  • Compressive sensing
  • Diffusion MRI
  • Fiber orientation
  • Linear unmixing
  • Sparse Bayesian learning
  • Sparse signal recovery

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