Abstract
Model observers intended to predict the diagnostic performance of human observers should account for the effects of both quantum and anatomical noise. We compared the abilities of several visual-search (VS) and scanning Hotelling-type models to account for anatomical noise in a localization receiver operating characteristic (LROC) study involving simulated nuclear medicine images. Our VS observer invoked a two-stage process of search and analysis. The images featured lesions in the prostate and pelvic lymph nodes. Lesion contrast and the geometric resolution and sensitivity of the imaging collimator were the study variables. A set of anthropomorphic mathematical phantoms was imaged with an analytic projector based on eight parallel-hole collimators with different sensitivity and resolution properties. The LROC study was conducted with human observers and the channelized nonprewhitening, channelized Hotelling (CH) and VS model observers. The CH observer was applied in a "background-known-statistically" protocol while the VS observer performed a quasi-background-known-exactly task. Both of these models were applied with and without internal noise in the decision variables. A perceptual search threshold was also tested with the VS observer. The model observers without inefficiencies failed to mimic the average performance trend for the humans. The CH and VS observers with internal noise matched the humans primarily at low collimator sensitivities. With both internal noise and the search threshold, the VS observer attained quantitative agreement with the human observers. Computational efficiency is an important advantage of the VS observer.
Highlights
Medical images are routinely used to identify lesions within the human body
With the high-contrast lesions, the model observers showed some qualitative similarities with the humans for the low-sensitivity collimators, but did not match the substantial drop in human performance that occurred with increased sensitivity
We investigated how the VS observer responded with internal noise and the search threshold together
Summary
Medical images are routinely used to identify lesions within the human body These images are read by radiologists, who generally make the final judgment about the presence of a lesion. It is expensive and time consuming to conduct human-observer studies for this purpose at every stage of developmental research. This has led to the development of mathematical model observers as surrogates for humans in diagnostic imaging studies. Of particular interest are model observers which can predict human performance in clinically realistic tasks involving lesion search. Such tasks should probe how quantum and anatomical noise in the images affect observer performance.
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