Algebraic Iterative Reconstruction-reprojection (AIRR) Method for High Performance Sparse-view CT Reconstruction

Document Type

Article

Publication Date

1-1-2016

Publication Title

Applied Mathematics and Information Sciences

Volume

10

Issue

6

First page number:

2007

Last page number:

2014

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. © 2016 NSP.

Keywords

Algebraic computed tomography; Iterative reconstruction-reprojection; Sparse-view CT reconstruction

Language

English

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