Mass detection in breast tomosynthesis and digital mammography: a model observer study

In this study, we adapt and apply model observers within the framework of realistic detection tasks in breast tomosynthesis (BT). We use images consisting of realistic masses digitally embedded in real patient anatomical backgrounds, and we adapt specific model observers that have been previously applied to digital mammography (DM). We design alternative forced-choice experiments (AFC) studies for DM and BT tasks in the signal known exactly but variable (SKEV) framework. We compare performance of various linear model observers (non-prewhitening matched filter with an eye filter, and several channelized Hotelling observers (CHO) against human. A good agreement in performance between human and model observers can be obtained when an appropriate internal noise level is adopted. Models achieve the same detection performance across BT and DM with about three times less projected signal intensity in BT than in DM (humans: 3.8), due to the anatomical noise reduction in BT. We suggest that, in the future, model observers can potentially be used as an objective tool for automating the optimization of BT acquisition parameters or reconstruction algorithms, or narrowing a wide span of possible parameter combinations, without requiring human observers studies.