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

Abstract : 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.
Complete list of metadatas

Cited literature [15 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01490554
Contributor : Li Wang <>
Submitted on : Wednesday, March 15, 2017 - 3:05:25 PM
Last modification on : Tuesday, November 6, 2018 - 9:24:43 AM

Identifiers

  • HAL Id : hal-01490554, version 1

Citation

Li Wang, Ali Mohammad-Djafari, Nicolas Gac, Mircea Dumitru. 3D X-ray Computed Tomography reconstruction using sparsity enforcing Hierarchical Model based on Haar Transformation. The 2017 International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Jun 2017, Xi'an, China. pp.295-298. ⟨hal-01490554⟩

Share

Metrics

Record views

26

Files downloads

33