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

Publication ,  Chapter
Pisharady, PK; Duarte-Carvajalino, JM; Sotiropoulos, SN; Sapiro, G; Lenglet, C
October 2015

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.

Duke Scholars

DOI

Publication Date

October 2015

Volume

9349

Start / End Page

117 / 124

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

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Pisharady, P. K., Duarte-Carvajalino, J. M., Sotiropoulos, S. N., Sapiro, G., & Lenglet, C. (2015). Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI. (Vol. 9349, pp. 117–124). https://doi.org/10.1007/978-3-319-24553-9_15
Pisharady, Pramod Kumar, Julio M. Duarte-Carvajalino, Stamatios N. Sotiropoulos, Guillermo Sapiro, and Christophe Lenglet. “Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI.,” 9349:117–24, 2015. https://doi.org/10.1007/978-3-319-24553-9_15.
Pisharady PK, Duarte-Carvajalino JM, Sotiropoulos SN, Sapiro G, Lenglet C. Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI. In 2015. p. 117–24.
Pisharady, Pramod Kumar, et al. Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI. Vol. 9349, 2015, pp. 117–24. Epmc, doi:10.1007/978-3-319-24553-9_15.
Pisharady PK, Duarte-Carvajalino JM, Sotiropoulos SN, Sapiro G, Lenglet C. Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI. 2015. p. 117–124.

DOI

Publication Date

October 2015

Volume

9349

Start / End Page

117 / 124

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences