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Iterative CT image reconstruction using neural network optimization algorithms
Author(s): Jun Zhang; Hongquan Zuo
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Paper Abstract

Stochastic or model-based iterative reconstruction is able to account for the stochastic nature of the CT imaging process and some artifacts and is able to provide better reconstruction quality. It is also, however, computationally expensive. In this work, we investigated the use of some of the neural network training algorithms such as momentum and Adam for iterative CT image reconstruction. Our experimental results indicate that these algorithms provide better results and faster convergence than basic gradient descent. They also provide competitive results to coordinate descent (a leading technique for iterative reconstruction) but, unlike coordinate descent, they can be implemented as parallel computations, hence can potentially accelerate iterative reconstruction in practice.

Paper Details

Date Published: 1 March 2019
PDF: 8 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094863 (1 March 2019); doi: 10.1117/12.2512329
Show Author Affiliations
Jun Zhang, Univ. of Wisconsin-Milwaukee (United States)
Hongquan Zuo, Univ. of Wisconsin-Milwaukee (United States)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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