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Journal of Medical Imaging • Open Access

Optimization of energy window and evaluation of scatter compensation methods in myocardial perfusion SPECT using the ideal observer with and without model mismatch and an anthropomorphic model observer
Author(s): Michael Ghaly; Jonathan M. Links; Eric Frey

Paper Abstract

We used the ideal observer (IO) and IO with model mismatch (IO-MM) applied in the projection domain and an anthropomorphic channelized Hotelling observer (CHO) applied to reconstructed images to optimize the acquisition energy window width and to evaluate various scatter compensation methods in the context of a myocardial perfusion single-photon emission computed tomography (SPECT) defect detection task. The IO has perfect knowledge of the image formation process and thus reflects the performance with perfect compensation for image-degrading factors. Thus, using the IO to optimize imaging systems could lead to suboptimal parameters compared with those optimized for humans interpreting SPECT images reconstructed with imperfect or no compensation. The IO-MM allows incorporating imperfect system models into the IO optimization process. We found that with near-perfect scatter compensation, the optimal energy window for the IO and CHO was similar; in its absence, the IO-MM gave a better prediction of the optimal energy window for the CHO using different scatter compensation methods. These data suggest that the IO-MM may be useful for projection-domain optimization when MM is significant and that the IO is useful when followed by reconstruction with good models of the image formation process.

Paper Details

Date Published: 9 March 2015
PDF: 14 pages
J. Med. Img. 2(1) 015502 doi: 10.1117/1.JMI.2.1.015502
Published in: Journal of Medical Imaging Volume 2, Issue 1
Show Author Affiliations
Michael Ghaly, Russell H. Morgan Dept. of Radiology & Radiological Science (United States)
Jonathan M. Links, Johns Hopkins Univ. (United States)
Eric Frey, Russell H. Morgan Dept. of Radiology & Radiological Science (United States)

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