Acceleration of MAP-EM algorithm via over-relaxation

Yu Jung Tsai; Hsuan Ming Huang; Yu Hua Dean Fang; Shi Ing Chang; Ing Tsung Hsiao

doi:10.1016/j.compmedimag.2014.11.004

Acceleration of MAP-EM algorithm via over-relaxation

Yu Jung Tsai, Hsuan Ming Huang, Yu Hua Dean Fang, Shi Ing Chang, Ing Tsung Hsiao

Department of Biomedical Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

To improve the convergence rate of the effective maximum a posteriori expectation-maximization (MAP-EM) algorithm in tomographic reconstructions, this study proposes a modified MAP-EM which uses an over-relaxation factor to accelerate image reconstruction. The proposed method, called MAP-AEM, is evaluated and compared with the results for MAP-EM and for an ordered-subset algorithm, in terms of the convergence rate and noise properties. The results show that the proposed method converges numerically much faster than MAP-EM and with a speed that is comparable to that for an ordered-subset type method. The proposed method is effective in accelerating MAP-EM tomographic reconstruction.

Original language	English
Pages (from-to)	100-107
Number of pages	8
Journal	Computerized Medical Imaging and Graphics
Volume	40
DOIs	https://doi.org/10.1016/j.compmedimag.2014.11.004
Publication status	Published - 2015 Mar 1

All Science Journal Classification (ASJC) codes

Radiological and Ultrasound Technology
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Health Informatics
Computer Graphics and Computer-Aided Design

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title = "Acceleration of MAP-EM algorithm via over-relaxation",

abstract = "To improve the convergence rate of the effective maximum a posteriori expectation-maximization (MAP-EM) algorithm in tomographic reconstructions, this study proposes a modified MAP-EM which uses an over-relaxation factor to accelerate image reconstruction. The proposed method, called MAP-AEM, is evaluated and compared with the results for MAP-EM and for an ordered-subset algorithm, in terms of the convergence rate and noise properties. The results show that the proposed method converges numerically much faster than MAP-EM and with a speed that is comparable to that for an ordered-subset type method. The proposed method is effective in accelerating MAP-EM tomographic reconstruction.",

author = "Tsai, {Yu Jung} and Huang, {Hsuan Ming} and Fang, {Yu Hua Dean} and Chang, {Shi Ing} and Hsiao, {Ing Tsung}",

year = "2015",

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doi = "10.1016/j.compmedimag.2014.11.004",

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AU - Tsai, Yu Jung

AU - Huang, Hsuan Ming

AU - Fang, Yu Hua Dean

AU - Chang, Shi Ing

AU - Hsiao, Ing Tsung

PY - 2015/3/1

Y1 - 2015/3/1

N2 - To improve the convergence rate of the effective maximum a posteriori expectation-maximization (MAP-EM) algorithm in tomographic reconstructions, this study proposes a modified MAP-EM which uses an over-relaxation factor to accelerate image reconstruction. The proposed method, called MAP-AEM, is evaluated and compared with the results for MAP-EM and for an ordered-subset algorithm, in terms of the convergence rate and noise properties. The results show that the proposed method converges numerically much faster than MAP-EM and with a speed that is comparable to that for an ordered-subset type method. The proposed method is effective in accelerating MAP-EM tomographic reconstruction.

AB - To improve the convergence rate of the effective maximum a posteriori expectation-maximization (MAP-EM) algorithm in tomographic reconstructions, this study proposes a modified MAP-EM which uses an over-relaxation factor to accelerate image reconstruction. The proposed method, called MAP-AEM, is evaluated and compared with the results for MAP-EM and for an ordered-subset algorithm, in terms of the convergence rate and noise properties. The results show that the proposed method converges numerically much faster than MAP-EM and with a speed that is comparable to that for an ordered-subset type method. The proposed method is effective in accelerating MAP-EM tomographic reconstruction.

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