3D X-ray Computed Tomography reconstruction using sparsity enforcing Hierarchical Model based on Haar Transformation

Li Wang, Ali Mohammad-Djafari, Nicolas Gac, Mircea Dumitru

DOI:10.12059/Fully3D.2017-11-3201013

Published in:Fully3D 2017 Proceedings

Pages:295-298

Keywords:
computed tomography (CT), Bayesian approach, hierarchical model, generalized Student-t distribution, joint maximum a posterior (JMAP)
In this paper, we consider the 3D X-ray CT reconstruction problem by using the Bayesian approach with a hierarchical prior model. A generalized Student-t distributed prior model is used to enforce the sparse structure of the multilevel Haar Transformation of the image. Comparisons with some state of the art methods are presented, showing that the proposed method gives more accurate reconstruction results and a faster convergence. Simulation results are also provided to show the effectiveness of the proposed hierarchical model for a reconstruction with more limited projections.
Li Wang
Laboratoire des signaux et systemes (L2S) - CentraleSupelec
Ali Mohammad-Djafari
CNRS, Laboratoire des Signaux et Systèmes
Nicolas Gac
Laboratoire des Signaux et Systèmes
Mircea Dumitru
Laboratoire des Signaux et Systèmes
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