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New iterative reconstruction methods for fan-beam tomography.
- Source :
-
Inverse Problems in Science & Engineering . Jun2018, Vol. 26 Issue 6, p773-791. 19p. - Publication Year :
- 2018
-
Abstract
- In this paper, we present a novel class of iterative reconstruction methods for severely angular undersampled or/and limited-view tomographic problems with fan-beam scanning geometry. The proposed algorithms are based on a new analytical transform which generalizes Fourier-slice theorem to divergent-beam scanning geometries. Using a non-rigid coordinate transform, divergent rays can be reorganized into parallel ones. Therefore, one can employ a simpler parallel-beam projection model instead of more complicated divergent-beam geometries. Various existing iterative reconstruction techniques for divergent-beam geometries can be easily adapted to the proposed framework. The significant advantage of this formulation is the possibility of exploiting efficient Fourier-based recovery methods without rebinning of the projections. In case of highly sparse measurements (few-view data), rebinning methods are not suitable due to error-prone angular interpolation involved. In this work, three new methods based on the novel analytical framework for fan-beam geometry are presented: the Gerchberg-Papoulis algorithm, the Neumann decomposition method and its total variation regularized version. Presented numerical experiments demonstrate that the methods can be competitive in reconstructing from few-view noisy tomographic measurements. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 17415977
- Volume :
- 26
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Inverse Problems in Science & Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 128618048
- Full Text :
- https://doi.org/10.1080/17415977.2017.1340946