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Algebraic Iterative Reconstruction-Reprojection (AIRR) Method for High Performance Sparse-View CT Reconstruction |
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PP: 2007-2014 |
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doi:10.18576/amis/100602
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Author(s) |
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Ali Pour Yazdanpanah,
Emma E. Regentova,
George Bebis,
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Abstract |
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| 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. |
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