An Improved Ordered-Subset Simultaneous Algebraic Reconstruction Technique

An Improved Ordered-Subset Simultaneous Algebraic Reconstruction Technique

Ordered-subset simultaneous algebraic reconstruction technique (OS-SART) was studied by Ge Wang and Ming Jiang in 2004. It accelerate the convergence of SART, but it has some disadvantages, such as increasing the number of subsets accelerates iterative convergence, but there is a point beyond which...

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Hauptverfasser: Huihua Kong, Jinxiao Pan
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Acceleration
Computed tomography
Convergence
Image quality
Image reconstruction
Iterative algorithms
Iterative methods
Mathematics
Partitioning algorithms
Testing
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description Ordered-subset simultaneous algebraic reconstruction technique (OS-SART) was studied by Ge Wang and Ming Jiang in 2004. It accelerate the convergence of SART, but it has some disadvantages, such as increasing the number of subsets accelerates iterative convergence, but there is a point beyond which image quality degrades due to a lack of statistical information within subset. In this paper, a new method of subset partition based on statistical test is proposed as an improved OS-SART (IOS-SART). IOS-SART can automatically adjust the number of the subsets for each iteration according to the statistical information content within subset demanded by user. Numerical simulation and application to practical data demonstrate that this algorithm converge faster and can provide high quality reconstructed images after a small number of iterations.
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subjects Acceleration
Computed tomography
Convergence
Image quality
Image reconstruction
Iterative algorithms
Iterative methods
Mathematics
Partitioning algorithms
Testing
title An Improved Ordered-Subset Simultaneous Algebraic Reconstruction Technique
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