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Proceedings Paper

An accelerated and convergent iterative algorithm in image reconstruction
Author(s): Jianhua Yan; Jun Yu
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Paper Abstract

Positron emission tomography (PET) is becoming increasingly important in the field of medicine and biology. The maximum-likelihood expectation-maximization (ML-EM) algorithm is becoming more important than filtered back-projection (FBP) algorithm which can incorporate various physical models into image reconstruction scheme. However, ML-EM converges slowly. In this paper, we propose a new algorithm named AC-ML-EM (accelerated and convergent maximum likelihood expectation maximization) by introducing gradually decreasing correction factor into ML-EM. AC-ML-EM has a higher speed of convergence. Through the experiments of computer simulated phantom data and real phantom data, AC-ML-EM is shown faster and better quantitatively than conventional ML-EM algorithm.

Paper Details

Date Published: 1 May 2007
PDF: 8 pages
Proc. SPIE 6534, Fifth International Conference on Photonics and Imaging in Biology and Medicine, 65342Y (1 May 2007); doi: 10.1117/12.741443
Show Author Affiliations
Jianhua Yan, Huazhong Univ. of Science and Technology (China)
Jun Yu, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 6534:
Fifth International Conference on Photonics and Imaging in Biology and Medicine
Qingming Luo; Lihong V. Wang; Valery V. Tuchin; Min Gu, Editor(s)

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