Abstract 1656: Quantifying task performance with photon-processing detectors

Abstract Purpose. To estimate task performance (e.g., the ability to correctly detect a small lesion in a normal tissue) when the imaging system uses a new class of detectors, called photon-processing detectors. Experimental Procedure. We simulated a single-photon emission computed tomography (SPECT) system in which multiple cameras image a 3D object from different angles. Each camera uses multiple measurements (e.g., photomultiplier tube outputs) to perform estimation of event parameters (or “attributes”) of each detected gamma-ray photon. Photon attributes, such as position, energy and direction of propagation, are stored at full precision. The likelihood ratio applied to photon-processing data is used to assess task performance for the detection of a signal buried in a random background. Statistical methods (including Markov chain Monte Carlo estimation) are used to calculate the performance of the likelihood ratio on this task. Results. Our results show an improvement with respect to conventional (i.e., pixelated) detectors when photon-processing detectors are used. The area under the receiver operating characteristic (ROC) curve is used to assess task performance, and we use this figure-of-merit to compare photon-processing detectors with conventional detectors. Conclusions. Increased performance is observed in the detection of a small lesion when photon-processing detectors are used instead of pixelated detectors. Applications that will benefit from photon-processing detectors include tumor diagnosis, drug development, therapy assessment and the study of tumor metabolism. Citation Format: Luca Caucci, Nicholas P. Henscheid, Harrison H. Barrett. Quantifying task performance with photon-processing detectors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1656.