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Hybrid algorithm of maximum-likelihood expectation-maximization and multiplicative algebraic reconstruction technique for iterative tomographic image reconstruction
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Paper Abstract

Maximum-likelihood expectation-maximization (ML-EM) method and multiplicative algebraic reconstruction technique (MART), which are well-known iterative image reconstruction algorithms, produce relatively highquality performance but each of which has an advantage and disadvantage. In this paper, in order to compensate for both disadvantages, we present a novel iterative algorithm constructed by a nonautonomous iterative system derived from the minimization of an α-skew Kullback–Leibler divergence, which is considered as a combined objective function for ML-EM and MART. We confirmed effectiveness of the proposed hybrid method through numerical experiments.

Paper Details

Date Published: 22 March 2019
PDF: 4 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110491F (22 March 2019); doi: 10.1117/12.2521185
Show Author Affiliations
Ryosuke Kasai, Tokushima Univ. (Japan)
Yusaku Yamaguchi, Shikoku Medical Ctr. for Children and Adults (Japan)
Takeshi Kojima, Tokushima Univ. (Japan)
Tetsuya Yoshinaga, Tokushima Univ. (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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