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A novel reconstruction algorithm to extend the CT scan field‐of‐view
Author(s) -
Hsieh J.,
Chao E.,
Thibault J.,
Grekowicz B.,
Horst A.,
McOlash S.,
Myers T. J.
Publication year - 2004
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1776673
Subject(s) - imaging phantom , robustness (evolution) , projection (relational algebra) , truncation (statistics) , iterative reconstruction , algorithm , computer vision , scanner , artificial intelligence , tomography , computer science , attenuation , image quality , mathematics , field of view , image (mathematics) , optics , biochemistry , chemistry , physics , machine learning , gene
For various reasons, a projection dataset acquired on a computed tomography (CT) scanner can be truncated. That is, a portion of the scanned object is positioned outside the scan field‐of‐view (SFOV) and the line integrals corresponding to those regions are not measured. A projection truncation problem causes imaging artifacts that lead to suboptimal image quality. In this paper, we propose a reconstruction algorithm that enables an adequate estimation of the projection outside the SFOV. We make use of the fact that the total attenuation of each ideal projection in a parallel sampling geometry remains constant over views. We use the magnitudes and slopes of the projection samples at the location of truncation to estimate water cylinders that can best fit to the projection data outside the SFOV. To improve the robustness of the algorithm, continuity constraints are placed on the fitting parameters. Extensive phantom and patient experiments were conducted to test the robustness and accuracy of the proposed algorithm.