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Proceedings Paper

A new series expansion method and its application to photon-counting CT reconstruction
Author(s): Mats U. Persson; Lin Fu; Peter M. Edic; Bruno De Man
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Paper Abstract

The introduction of photon-counting detectors in x-ray computed tomography raises the question of how reconstruction algorithms should be adapted to photon-counting measurement data. The transition from energyintegrating to photon-counting detectors introduces new effects into the data model, such as pure Poisson statistics and increased cross talk between detector pixels, (e.g. due to charge sharing), but it is still not known in detail how these effects can be treated accurately by the reconstruction algorithm. In this work, we propose a new reconstruction method based on penalized-likelihood reconstruction that incorporates these effects. By starting from a simple, easily-solved reconstruction problem and adding correction terms for the additional physical effects, we obtain a series expansion for the solution to the image reconstruction problem. This approach serves the twofold purpose of (1) yielding a new, potentially faster method of incorporating complex detector models in the reconstruction process and (2) providing insight into the impact of the non-ideal physical effects on the reconstructed image. We investigate the potential for reconstructing images from simulated photon-counting energy-resolving CT data with the new algorithm by including correction terms representing pure Poisson statistics and interpixel cross talk; and we investigate the impact of these physical effects on the reconstructed images. Results indicate that using two correction terms gives good agreement with the converged solution, suggesting that the new method is feasible in practice. This new approach to image reconstruction can help in developing improved reconstruction algorithms for photon-counting CT.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113121H (16 March 2020); doi: 10.1117/12.2549623
Show Author Affiliations
Mats U. Persson, KTH Royal Institute of Technology (Sweden)
Lin Fu, GE Research (United States)
Peter M. Edic, GE Research (United States)
Bruno De Man, GE Research (United States)


Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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