Iterative image reconstruction with random correction for PET studies

Jyh Cheng Chen*, Ren Shyan Liu, Kao Yin Tu, Henry Horng Shing Lu, Tai Been Chen, Kuo Liang Chou

*Corresponding author for this work

Research output: Contribution to journalConference article

1 Scopus citations

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.

Original languageEnglish
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3979
DOIs
StatePublished - 1 Jan 2000
EventMedical Imaging 2000: Image Processing - San Diego, CA, USA
Duration: 14 Feb 200017 Feb 2000

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