Performance of list mode Hotelling observer and comparison to a neural network observer

Abstract

The Hotelling observer (HO) is a commonly used linear observer for detection or classification tasks. The conventional implementation operating on binned data normally involves inversion of covariance matrices and estimation of the difference in means of two vectors. However, the conventional calculation can t be directly applied to list mode data. The situation is salvageable by using the attribute list to construct a Poisson point process in attribute space,1 which makes the computation of HO quite different. In this work, we present an example of computing the HO test statistic on list mode data. The observer performance is measured on a signalknown-exactly and background-known-statistically task. The receiver operating characteristic (ROC) curve of the HO on list mode data is compared to the corresponding approximation by use of supervised learning methods proposed in the paper2 on binned data, where a single-layer neural network (SLNN) is used to approximate the HO test statistic. The comparison shows that the HO on list mode data outperforms the binned data. The result demonstrates the fact again that list mode data contains more information comparing to its binned version. 2022 SPIE. © 2022 SPIE. All rights reserved.