AIR Tools II: algebraic iterative reconstruction methods, improved implementation

We present a MATLAB software package with efficient, robust, and flexible implementations of algebraic iterative reconstruction (AIR) methods for computing regularized solutions to discretizations of inverse problems. These methods are of particular interest in computed tomography and similar proble...

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Veröffentlicht in:Numerical algorithms Jg. 79; H. 1; S. 107 - 137
Hauptverfasser: Hansen, Per Christian, Jørgensen, Jakob Sauer
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2018
Springer Nature B.V
Schlagworte:
Algebra
Algorithms
Computed tomography
Computer Science
Image reconstruction
Inverse problems
Iterative methods
Matrix algebra
Modular design
Numeric Computing
Numerical Analysis
Original Paper
Software packages
Theory of Computation
Tomography
ART methods
Column-action methods
Stopping rules
Algebraic iterative reconstruction
Semi-convergence
65F10
65F22
Tomographic imaging
SIRT methods
ISSN:1017-1398, 1572-9265
Online-Zugang:Volltext
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Zusammenfassung:We present a MATLAB software package with efficient, robust, and flexible implementations of algebraic iterative reconstruction (AIR) methods for computing regularized solutions to discretizations of inverse problems. These methods are of particular interest in computed tomography and similar problems where they easily adapt to the particular geometry of the problem. All our methods are equipped with stopping rules as well as heuristics for computing a good relaxation parameter, and we also provide several test problems from tomography. The package is intended for users who want to experiment with algebraic iterative methods and their convergence properties. The present software is a much expanded and improved version of the package AIR Tools from 2012, based on a new modular design. In addition to improved performance and memory use, we provide more flexible iterative methods, a column-action method, new test problems, new demo functions, and—perhaps most important—the ability to use function handles instead of (sparse) matrices, allowing larger problems to be handled.
Bibliographie:ObjectType-Article-1
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ISSN:1017-1398
1572-9265
DOI:10.1007/s11075-017-0430-x