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Proceedings Paper

Improving lesion detectability in PET imaging with a penalized likelihood reconstruction algorithm
Author(s): Kristen A. Wangerin; Sangtae Ahn; Steven G. Ross; Paul E. Kinahan; Ravindra M. Manjeshwar
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Paper Abstract

Ordered Subset Expectation Maximization (OSEM) is currently the most widely used image reconstruction algorithm for clinical PET. However, OSEM does not necessarily provide optimal image quality, and a number of alternative algorithms have been explored. We have recently shown that a penalized likelihood image reconstruction algorithm using the relative difference penalty, block sequential regularized expectation maximization (BSREM), achieves more accurate lesion quantitation than OSEM, and importantly, maintains acceptable visual image quality in clinical wholebody PET. The goal of this work was to evaluate lesion detectability with BSREM versus OSEM. We performed a twoalternative forced choice study using 81 patient datasets with lesions of varying contrast inserted into the liver and lung. At matched imaging noise, BSREM and OSEM showed equivalent detectability in the lungs, and BSREM outperformed OSEM in the liver. These results suggest that BSREM provides not only improved quantitation and clinically acceptable visual image quality as previously shown but also improved lesion detectability compared to OSEM. We then modeled this detectability study, applying both nonprewhitening (NPW) and channelized Hotelling (CHO) model observers to the reconstructed images. The CHO model observer showed good agreement with the human observers, suggesting that we can apply this model to future studies with varying simulation and reconstruction parameters.

Paper Details

Date Published: 17 March 2015
PDF: 8 pages
Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 94160W (17 March 2015); doi: 10.1117/12.2082301
Show Author Affiliations
Kristen A. Wangerin, General Electric Global Research Ctr. (United States)
Univ. of Washington (United States)
Sangtae Ahn, General Electric Global Research Ctr. (United States)
Steven G. Ross, General Electric Healthcare (United States)
Paul E. Kinahan, Univ. of Washington (United States)
Ravindra M. Manjeshwar, General Electric Global Research Ctr. (United States)

Published in SPIE Proceedings Vol. 9416:
Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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