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Article Dans Une Revue IEEE Transactions on Radiation and Plasma Medical Sciences Année : 2022

Region-of-Interest CT Reconstruction using Object Extent and Singular Value Decomposition

Résumé

In computed tomography, a whole scan of the object may be impossible, generally because the object is larger than the scanner field-of-view. Such a set up leads to truncated projections. Using differentiated backprojection, the reconstruction problem can be reduced to a set of one-dimensional problems consisting of the inversion of the Hilbert transform. When the object partly overlaps the scanner field-of-view, this problem is commonly referred to as the "one-sided truncated Hilbert transform". Our work investigates this situation and proposes a novel approach to address it. Using differentiated backprojection, and the object extent supposedly known a priori, a pseudo-inverse of the truncated Hilbert transform is computed by truncated singular value decomposition, and its truncated singular values are replaced by a simple estimation. The estimation is calculated using the singular value decomposition of the known convex hull filled with a constant value per line computed from the corresponding projection in the direction of the Hilbert transform. Experiments illustrate the image quality improvements resulting from this approach compared to a simple truncation of the singular values and the reconstruction speed improvement compared to two-dimensional iterative reconstruction solving penalized least squares with the conjugate gradient algorithm.
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Dates et versions

hal-03275441 , version 1 (01-07-2021)
hal-03275441 , version 2 (24-08-2021)

Identifiants

Citer

Aurelien Coussat, Simon Rit, Rolf Clackdoyle, Michel Defrise, Laurent Desbat, et al.. Region-of-Interest CT Reconstruction using Object Extent and Singular Value Decomposition. IEEE Transactions on Radiation and Plasma Medical Sciences, 2022, 6 (5), pp.537-551. ⟨10.1109/TRPMS.2021.3091288⟩. ⟨hal-03275441v2⟩
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