Bayesian X-ray Computed Tomography using a Three-level Hierarchical Prior Model

Abstract : In recent decades X-ray Computed Tomography (CT) image reconstruction has been largely developed in both medical and industrial domain. In this paper, we propose using the Bayesian inference approach with a new hierarchical prior model. In the proposed model, a generalised Student-t distribution is used to enforce the Haar transformation of images to be sparse. Comparisons with some state of the art methods are presented. It is shown that by using the proposed model, the sparsity of the sparse representation of images is enforced, so that edges of images are preserved. Simulation results are also provided to demonstrate the effectiveness of the new hierarchical model for reconstruction with fewer projections.
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Submitted on : Sunday, November 27, 2016 - 6:36:41 PM
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Li Wang, Ali Mohammad-Djafari, Nicolas Gac. Bayesian X-ray Computed Tomography using a Three-level Hierarchical Prior Model. AIP Conference, Bayesian inference and maximum entropy methods in science and engineering (Maxent 2016), Jul 2016, Gent, Belgium. ⟨10.1063/1.4985361 ⟩. ⟨hal-01403790⟩



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