Algebraic Iterative Reconstruction-Reprojection (AIRR) Method for High Performance Sparse-View CT Reconstruction

Authors

Ali Pour Yazdanpanah, Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USAFollow
Emma E. Regentova, Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USAFollow
George Bebis, Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USAFollow

Author Country (or Countries)

USA

Abstract

The reconstruction from sparse- or few-view projections is one of important problems in computed tomography limited by the availability or feasibility of a large number of projections. Working with a small number of projections provides a lower radiation dose and a fast scan time, however an error associated with the sparse-view reconstruction increases significantly as the space sparsity increases that may cause the reconstruction process to diverge. The iterative reconstruction-re-projection (IRR) algorithm which uses filtered back projection (FBP) reconstruction has been used for the sparse-view computed tomography applications for several years. The IRR-TV method has been developed as a higher performance alternative to the IRR method by adding the total variation (TV) minimization. Here, we propose an algebraic iterative reconstruction-re-projection (AIRR) algorithm with the shearlet regularization. The AIRR coupled with the shearlet regularization in image space attains a better estimation in the projection space and yielded a higher performance based on subjective and objective quality metrics.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/100602

Recommended Citation

Pour Yazdanpanah, Ali; E. Regentova, Emma; and Bebis, George (2016) "Algebraic Iterative Reconstruction-Reprojection (AIRR) Method for High Performance Sparse-View CT Reconstruction," Applied Mathematics & Information Sciences: Vol. 10: Iss. 6, Article 2.
DOI: http://dx.doi.org/10.18576/amis/100602
Available at: https://digitalcommons.aaru.edu.jo/amis/vol10/iss6/2