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20 March 2007 An advanced system model for the prediction of the clinical task performance of radiographic systems
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A flexible software tool was developed that combines predictive models for detector noise and blur with image simulation and an improved human observer model to predict the clinical task performance of existing and future radiographic systems. The model starts with high-fidelity images from a database and mathematical models of common disease features, which may be added to the images at desired contrast levels. These images are processed through the entire imaging chain including capture, the detector, image processing, and hardcopy or softcopy display. The simulated images and the viewing conditions are passed to a human observer model, which calculates the detectability index d' of the signal (disease or target feature). The visual model incorporates a channelized Hotelling observer with a luminance-dependent contrast sensitivity function and two types of internal visual system noise (intrinsic and image background-induced). It was optimized based on three independent human observer studies of target detection, and is able to predict d' over a wide range of viewing conditions, background complexities, and target spatial frequency content. A more intuitive metric of system performance, Task-Specific Detective Efficiency (TSDE), is defined to indicate how much detector improvements would translate to better radiologist performance. The TSDE is calculated as the squared ratio of d' for a system with the actual detector and a hypothetical system containing an ideal detector. A low TSDE, e.g., 5% for the detection of 0.1 mm microcalcifications in typical mammography systems, indicates that improvements in the detector characteristics are likely to translate to better detection performance. The TSDE of lung nodule detection is as high as 75% even with the detective quantum efficiency (DQE) of the detector not exceeding 24%. Applications of the model to system optimizations for flat-panel detectors, in mammography and dual energy digital radiography, are discussed.
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Karin Töpfer, Brian W. Keelan, and Francisca Sugiro "An advanced system model for the prediction of the clinical task performance of radiographic systems", Proc. SPIE 6515, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, 651512 (20 March 2007);

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