Algebraic iterative reconstruction-reprojection (AIRR) method for high performance sparse-view CT reconstruction
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.
Algebraic iterative reconstruction-reprojection (AIRR) method for high performance sparse-view CT reconstruction.
Applied Mathematics and Information Sciences, 10(6),