  Model observers have a long history of success in predicting human observer
performance in clinically-relevant detection tasks. New 3D image modalities
provide more signal information but vastly increase the search space to be
scrutinized. Here, we compared standard linear model observers (ideal
observers, non-pre-whitening matched filter with eye filter, and various
versions of Channelized Hotelling models) to human performance searching in 3D
1/f$^{2.8}$ filtered noise images and assessed its relationship to the more
traditional location known exactly detection tasks and 2D search. We
investigated two different signal types that vary in their detectability away
from the point of fixation (visual periphery). We show that the influence of 3D
search on human performance interacts with the signal's detectability in the
visual periphery. Detection performance for signals difficult to detect in the
visual periphery deteriorates greatly in 3D search but not in 3D location known
exactly and 2D search. Standard model observers do not predict the interaction
between 3D search and signal type. A proposed extension of the Channelized
Hotelling model (foveated search model) that processes the image with reduced
spatial detail away from the point of fixation, explores the image through eye
movements, and scrolls across slices can successfully predict the interaction
observed in humans and also the types of errors in 3D search. Together, the
findings highlight the need for foveated model observers for image quality
evaluation with 3D search.
