Unsupervised sparsity enforcing iterative algorithms for 3D image reconstruction in X-ray computed tomography

Abstract : Unsupervised iterative reconstruction algorithms based on a Bayesian approach for piecewise constant images are presented in this paper. Such images can be expressed via a sparse representation and the reconstruction problem can be addressed using sparsity enforcing priors. We focus on sparsity enforcing priors expressed as Normal variance mixture, considering three mixing distributions: Inverse Gamma distribution, corresponding to Student-t prior, general inverse Gaussian distribution with the real parameter fixed, corresponding to Normal-inverse Gaussian prior and Gamma distribution corresponding to Variance-Gamma prior. We present and discuss the corresponding iterative algorithms considering the Joint Maximum A Posteriori estimation showing simulations results for 3D X-ray Computed Tomography.
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https://hal.archives-ouvertes.fr/hal-01568325
Contributor : Li Wang <>
Submitted on : Tuesday, July 25, 2017 - 10:38:37 AM
Last modification on : Thursday, October 25, 2018 - 11:34:16 AM

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  • HAL Id : hal-01568325, version 1

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Mircea Dumitru, Nicolas Gac, Li Wang, Ali Mohammad-Djafari. Unsupervised sparsity enforcing iterative algorithms for 3D image reconstruction in X-ray computed tomography. The 2017 International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Jun 2017, Xi'an, China. pp.359-362. ⟨hal-01568325⟩

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