Aperture optimization in emission imaging using ideal observers for joint detection and localization.
For the familiar 2-class detection problem (signal present/absent), ideal observers have been applied to optimization of pinhole and collimator parameters in planar emission imaging. Given photon noise and background and signal variabilities, such experiments show how to optimize an aperture to maximize detectability of the signal. Here, we consider a fundamentally different, more realistic task in which the observer is required to both detect and localize a signal. The signal is embedded in a variable background and is known except for location. We inquire whether the addition of a localization requirement changes conclusions on aperture optimization. We have previously formulated an ideal observer for this joint detection/localization task, and here apply it to the classic problem of determining an optimal pinhole diameter in a planar emission imaging system. We conclude that as search tolerance on localization decreases, the optimal pinhole diameter shrinks from that required by detection alone, and, in addition, task performance becomes more sensitive to fluctuations about the optimal pinhole diameter. As in the case for detection only, the optimal pinhole diameter shrinks as the amount of background variability grows and, in addition, conspicuity limits can be observed. Unlike the case for detection only, our task leads to a finite aperture size in the absence of background variability. For both tasks, the inclusion of background variability yields a finite aperture size.
Zhou
Lili
L
Department of Radiology, Stony Brook University, NY 11790, USA.
Khurd
Parmeshwar
P
Kulkarni
Santosh
S
Rangarajan
Anand
A
Gindi
Gene
G
D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D003952:Diagnostic Imaging / Q000295:instrumentation* / Q000379:methods*; D004867:Equipment Design; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D016012:Poisson Distribution; D012372:ROC Curve; D015203:Reproducibility of Results; D013997:Time Factors