Toward image quality assessment in mammography using model observers: Detection of a calcification‐like object

Purpose: Model observers (MOs) are of interest in the field of medical imaging to assess image quality. However, before procedures using MOs can be proposed in quality control guidelines for mammography systems, we need to know whether MOs are sensitive to changes in image quality and correlations in background structure. Therefore, as a proof of principle, in this study human and model observer (MO) performance are compared for the detection of calcification‐like objects using different background structures and image quality levels of unprocessed mammography images. Method: Three different phantoms, homogeneous polymethyl methacrylate, BR3D slabs with swirled patterns (CIRS, Norfolk, VA, USA), and a prototype anthropomorphic breast phantom (Institute of Medical Physics and Radiation Protection, Technische Hochschule Mittelhessen, Germany) were imaged on an Amulet Innovality (FujiFilm, Tokyo, Japan) mammographic X‐ray unit. Because the complexities of the structures of these three phantoms were different and not optimized to match the characteristics of real mammographic images, image processing was not applied in this study. In addition, real mammograms were acquired on the same system. Regions of interest (ROIs) were extracted from each image. In half of the ROIs, a 0.25‐mm diameter disk was inserted at four different contrast levels to represent a calcification‐like object. Each ROI was then modified, so four image qualities relevant for mammography were simulated. The signal‐present and signal‐absent ROIs were evaluated by a non‐pre‐whitening model observer with eye filter (NPWE) and a channelized Hotelling observer (CHO) using dense difference of Gaussian channels. The ROIs were also evaluated by human observers in a two alternative forced choice experiment. Detectability results for the human and model observer experiments were correlated using a mixed‐effect regression model. Threshold disk contrasts for human and predicted human observer performance based on the NPWE MO and CHO were estimated. Results: Global trends in threshold contrast were similar for the different background structures, but absolute contrast threshold levels differed. Contrast thresholds tended to be lower in ROIs from simple phantoms compared with ROIs from real mammographic images. The correlation between human and model observer performance was not affected by the range of image quality levels studied. Conclusions: The correlation between human and model observer performance does not depend on image quality. This is a promising outcome for the use of model observers in image quality analysis and allows for subsequent research toward the development of MO‐based quality control procedures and guidelines.