Aperture optimization in emission imaging using ideal observers for joint detection and localization

Abstract

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 variability, 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.

Collimator optimization in SPECT based on a joint detection and localization task

“…We presented initial work on this topic at two conferences (Zhou and Gindi 2008a) and (Zhou and Gindi 2008b). We also presented similar work but in the context of pinhole optimization for planar emission imaging systems in Zhou et al (2008). The purpose of this paper is to show a methodology for optimizing collimators in SPECT for this new type of task, and to present some initial results using this approach.…”

Optimal Joint Detection and Estimation That Maximizes ROC-Type Curves

“…One difficulty is that computational evaluation of the optimal decision rules given here may be highly complex, particularly for problems involving an unknown number of signals, because it generally requires evaluation of high-dimensional data likelihoods, multi-dimensional integration and nonconvex optimization. Nonetheless, computationally tractable methods might be possible in some scenarios, and techniques utilized in [21], [40]–[42] may be a good starting point for such research.…”

“…Such optimal strategies, commonly called “ideal observers” in the medical imaging literature [20], can potentially be utilized for imaging system optimization [21], [22], evaluation of observer efficiency [23]–[25], and development of image formation algorithms [23], [24]. For the above ROC-type curves, a higher summary curve implies better performance.…”

PET Energy-Based Scatter Estimation in the Presence of Randoms, and Image Reconstruction With Energy-Dependent Scatter and Randoms Corrections

“…Such metrics only partially capture the complexity of the signal detection task on a random field with correlated noise and signals at unknown positions. Moreover, it has been shown that in a device optimization problem the use of the known-location signal detection task could lead to different optimization values than the unknown-signal location detection task, which often is more clinically relevant [21]. For these reasons, image evaluation procedures using signal search are being used with increased frequency [22]- [25].…”