Development of an Ideal Observer that Incorporates Nuisance Parameters and Processes List-Mode Data

TL;DR

This paper develops an ideal observer model for list-mode data processing in binary discrimination tasks, incorporating nuisance parameters like object orientation and count-rate variability, evaluated through Monte Carlo simulations.

Contribution

The study introduces an ideal observer that accounts for nuisance parameters in list-mode data, improving discrimination performance in complex, uncertain scenarios.

Findings

Incorporating nuisance parameters enhances observer performance.

The model performs well under both known and unknown conditions.

Monte Carlo simulations validate the approach.

Abstract

Observer models were developed to process data in list-mode format in order to perform binary discrimination tasks for use in an arms-control-treaty context. Data used in this study was generated using GEANT4 Monte Carlo simulations for photons using custom models of plutonium inspection objects and a radiation imaging system. Observer model performance was evaluated and presented using the area under the receiver operating characteristic curve. The ideal observer was studied under both signal-known-exactly conditions and in the presence of unknowns such as object orientation and absolute count-rate variability; when these additional sources of randomness were present, their incorporation into the observer yielded superior performance.