Abstract
PURPOSE: Virtual imaging trials (VITs) are of interest for regulatory evaluation because they enable faster and more cost-effective evaluation of new imaging technologies than patient clinical trials. Our purpose is to develop a hybrid VIT methodology for breast computed tomography (CT) applications and use it to investigate microcalcification detectability. APPROACH: Ray tracing was used to generate projection images of clusters of five microcalcifications which varied in diameter, chemical composition, and density. These simulated projection images were added to patient projection images acquired with the fourth-generation breast CT scanner from UC Davis (Doheny) and reconstructed using the Feldkamp filtered backprojection algorithm with varying apodization kernels. Volumes of interest and maximum intensity projections were extracted from the reconstructed volumes. Human observers (HOs) and deep learning model observers (DLMOs) were used to detect calcification clusters, and receiver operating characteristic curve analysis was used to analyze detection performance. RESULTS: DLMO detected 0.18-mm type I calcifications with AUC = 0.80 and 0.21 mm calcifications with AUC = 0.99 . HO performance was inferior to deep learning model observer performance, but both HO and DLMO detected 0.21-mm type I calcifications with AUC > 0.90 and 0.24-mm type I calcifications with near-perfect performance. Microcalcification clusters embedded in adipose tissue were more conspicuous than clusters embedded in fibroglandular tissue. There was superior detection performance for clusters located anteriorly within the breast compared with clusters located posteriorly within the breast. CONCLUSIONS: A hybrid approach for virtual imaging trials shows promise for the assessment of imaging systems across a broad range of parameters.