Scanning model observers to predict human performance in LROC studies of SPECT reconstruction using anatomical priors

摘要

We use scanning model observers to predict human performance in lesion search/detection study. The observer's task is to locate gallium-avid tumors in simulated SPECT images of a digital phantom. The goal of our model is to predict the optimal prior strength 0 for human observers of smoothing priors incorporated into the reconstruction algorithm. These priors use varying amounts of anatomical knowledge. We present results from a scanning channelized non-prewhitening matched filter, and compare them with results from a human-observer study. Including a step to mimic the greyscale perceptual-linearization used during the human-observer study improves the accuracy of the model. However we find that for lesions close to an organ boundary even the improved model does not accurately predict human performance.