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Proceedings Paper

Iterative image reconstruction with random correction for PET studies
Author(s): Jyh-Cheng Chen; Ren-Shyan Liu; Kao-Yin Tu; Henry Horng-Shing Lu; Tai-Been Chen; Kuo Liang Chou
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Paper Abstract

A maximum likelihood-expectation maximization (ML-EM) reconstruction algorithm has been developed that allows random coincidence correction for the phantom we used and the reconstructed images are better than those obtained by convolution backprojection (CBP) for positron emission tomography (PET) studies in terms of spatial resolution, image artifacts and noise. With our algorithm reconstruct the true coincidence events and random coincidence events were reconstructed separately. We also calculated the random ratio from the measured projection data (singles) using line and cylindrical phantoms, respectively. From cylindrical phantom experiments, the random event ratio was 41.8% to 49.1% in each ring. These results are close to the ratios obtained from geometric calculation, which range from 45.0% to 49.5%. The random ratios and the patterns of random events provide insightful information for random correction. This information is particularly valuable when the delay window correction is not available as in the case of our PET system.

Paper Details

Date Published: 6 June 2000
PDF: 12 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387629
Show Author Affiliations
Jyh-Cheng Chen, National Yang-Ming Univ. (Taiwan)
Ren-Shyan Liu, Taipei Veterans General Hospital (Taiwan)
Kao-Yin Tu, National Yang-Ming Univ. (Taiwan)
Henry Horng-Shing Lu, National Chiao Tung Univ. (Taiwan)
Tai-Been Chen, National Chiao Tung Univ. (Taiwan)
Kuo Liang Chou, Taipei Veterans General Hospital (Taiwan)


Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

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