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Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9349)

Abstract

The RubiX [1] algorithm combines high SNR characteristics of low resolution data with high spacial specificity of high resolution data, to extract microstructural tissue parameters from diffusion MRI. In this paper we focus on estimating crossing fiber orientations and introduce sparsity to the RubiX algorithm, making it suitable for reconstruction from compressed (under-sampled) data. We propose a sparse Bayesian algorithm for estimation of fiber orientations and volume fractions from compressed diffusion MRI. The data at high resolution is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible directions. Volume fractions of fibers along these orientations define the dictionary weights. The data at low resolution is modeled using a spatial partial volume representation. The proposed dictionary representation and sparsity priors consider the dependence between fiber orientations and the spatial redundancy in data representation. Our method exploits the sparsity of fiber orientations, therefore facilitating inference from under-sampled data. Experimental results show improved accuracy and decreased uncertainty in fiber orientation estimates. For under-sampled data, the proposed method is also shown to produce more robust estimates of fiber orientations.

Keywords

Sparse Bayesian inference Compressive sensing Linear un-mixing Diffusion MRI Fiber orientation Brain imaging 
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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CMRR, RadiologyUniversity of MinnesotaMinneapolisUSA
  2. 2.FMRIB, John Radcliffe HospitalUniversity of OxfordHeadingtonUK
  3. 3.Electrical and Computer EngineeringDuke UniversityDurhamUSA
  4. 4.Biomedical Engineering and Computer ScienceDuke UniversityDurhamUSA

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