Model and human observer reproducibility for detecting microcalcifications in digital breast tomosynthesis images

Abstract

Digital breast tomosynthesis (DBT) is a relatively new 3D breast imaging technique, which allows for better low contrast lesion detection than 2D full field digital mammography (FFDM). European guidelines for quality control in FFDM specify minimum and achievable threshold contrasts of small test inserts, determined from readings by human observers. Today model observers are being developed to predict and subsequently substitute human detectability readings. A similar performance test would be welcomed for DBT. However, since such a performance estimation is based on an observer classification, in order to circumvent misjudgments, it is important that the classification system is reliable. The aim of this study was to assess the human and model observer reliability by determining the observer reproducibility when reading 5 datasets from 60 tomosynthesis series acquired under the same conditions. For this purpose, a 3D structured phantom with calcification cluster models was scanned on a Siemens Inspiration tomosynthesis system. VOIs were extracted from these acquisitions and read under a 4 alternative forced choice (4-AFC) paradigm by 6 human observers. A channelized Hotelling model observer using 8 Laguerre-Gauss(LG) channels was developed including a scanning algorithm to detect the calcification clusters. An internal noise method was used to better approximate the human reading results. The observer reproducibility was estimated by bootstrapping and SEM was used as a figure of merit. The results show that the model observer is more reproducible for the smaller calcification sizes with maximum of 5.81 SEM, than human observer with maximum of 13.57 SEM. For the larger clusters both observers have similar reproducibility.