  By analyzing human readers' performance in detecting small round lesions in
simulated digital breast tomosynthesis background in a location known exactly
scenario, we have developed a model observer that is a better predictor of
human performance with different levels of background complexity (i.e.,
anatomical and quantum noise). Our analysis indicates that human observers
perform a lesion detection task by combining a number of sub-decisions, each an
indicator of the presence of a lesion in the image stack. This is in contrast
to a channelized Hotelling observer, where the detection task is conducted
holistically by thresholding a single decision variable, made from an optimally
weighted linear combination of channels. However, it seems that the sub-par
performance of human readers compared to the CHO cannot be fully explained by
their reliance on sub-decisions, or perhaps we do not consider a sufficient
number of sub-decisions. To bridge the gap between the performances of human
readers and the model observer based upon sub-decisions, we use an additive
noise model, the power of which is modulated with the level of background
complexity. The proposed model observer better predicts the fast drop in human
detection performance with background complexity.
