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

Publication ,  Journal Article
Pisharady, PK; Sotiropoulos, SN; Duarte-Carvajalino, JM; Sapiro, G; Lenglet, C
Published in: NeuroImage
February 2018

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.

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Published In

NeuroImage

DOI

EISSN

1095-9572

ISSN

1053-8119

Publication Date

February 2018

Volume

167

Start / End Page

488 / 503

Related Subject Headings

  • White Matter
  • Neurology & Neurosurgery
  • Neuroimaging
  • Nerve Fibers, Myelinated
  • Models, Theoretical
  • Image Interpretation, Computer-Assisted
  • Humans
  • Diffusion Magnetic Resonance Imaging
  • Bayes Theorem
  • Algorithms
 

Citation

APA
Chicago
ICMJE
MLA
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Pisharady, P. K., Sotiropoulos, S. N., Duarte-Carvajalino, J. M., Sapiro, G., & Lenglet, C. (2018). Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning. NeuroImage, 167, 488–503. https://doi.org/10.1016/j.neuroimage.2017.06.052
Pisharady, Pramod Kumar, Stamatios N. Sotiropoulos, Julio M. Duarte-Carvajalino, Guillermo Sapiro, and Christophe Lenglet. “Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning.NeuroImage 167 (February 2018): 488–503. https://doi.org/10.1016/j.neuroimage.2017.06.052.
Pisharady PK, Sotiropoulos SN, Duarte-Carvajalino JM, Sapiro G, Lenglet C. Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning. NeuroImage. 2018 Feb;167:488–503.
Pisharady, Pramod Kumar, et al. “Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning.NeuroImage, vol. 167, Feb. 2018, pp. 488–503. Epmc, doi:10.1016/j.neuroimage.2017.06.052.
Pisharady PK, Sotiropoulos SN, Duarte-Carvajalino JM, Sapiro G, Lenglet C. Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning. NeuroImage. 2018 Feb;167:488–503.
Journal cover image

Published In

NeuroImage

DOI

EISSN

1095-9572

ISSN

1053-8119

Publication Date

February 2018

Volume

167

Start / End Page

488 / 503

Related Subject Headings

  • White Matter
  • Neurology & Neurosurgery
  • Neuroimaging
  • Nerve Fibers, Myelinated
  • Models, Theoretical
  • Image Interpretation, Computer-Assisted
  • Humans
  • Diffusion Magnetic Resonance Imaging
  • Bayes Theorem
  • Algorithms