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 projectiondomain optimization when MM is significant and that the IO is useful when followed by reconstruction with good models of the image formation process.
Highlights
Model observers have been widely used to perform task-based assessment of medical image quality
We extended the work introduced in Ref. 36 to compare different scatter estimation methods, including the dual energy window (DEW), triple energy window (TEW), and effective source scatter estimation (ESSE) methods in the context of myocardial perfusion SPECT (MPS), and to find the optimal acquisition energy window width that provides the best performance on a binary defect detection task using the ideal observer (IO) and IO with model mismatch (IO-MM)
Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Medical-Imaging on 08 Nov 2021 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use widths was broader for the IO than for the other observers. This is consistent with the fact that the IO, which has the highest performance of any observer, implicitly has perfect knowledge of the image formation process and all physical image-degrading factors
Summary
Model observers have been widely used to perform task-based assessment of medical image quality. The ideal observer (IO) outperforms all other observers and sets an upper limit on task performance measured by figures of merit such as the area under the receiver operating characteristic (ROC) curve (AUC).[1] The IO makes optimal use of all the information in the raw data. It requires full knowledge of the raw data statistics. IO performance is not improved by invertible operations on the raw data such as linear filtering or (invertible) reconstruction algorithms, and it allows optimization of instrumentation or reconstruction parameters in the projection domain and provides an alternative to image-domain optimization
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