Multislice anthropomorphic model observer for detectability evaluation on breast cone beam CT images

摘要

We predict human observer performance for lesion detection on breast cone beam computed tomography(CBCT) images using single-slice and multislice model observers with a constant internal noise level. We evaluatehuman observer performance on single-slice and multislice simulated breast CBCT images with 1 mm signal, andpredict the performance using model observers. We use a channelized Hotelling observer (CHO) and nonprewhiteningobserver with eye-filter (NPWE). We employ dense difference-of-Gaussian (D-DOG) channels for CHO, and eyefilterwith peak value at 7 cyc/deg for NPWE. We include channel internal noise for CHO and decision variableinternal noise for NPWE. For single-slice images, D-DOG CHO and NPWE predict human observer performancewell. For multislice images, D-DOG CHO overestimates human observer performance, while NPWE predict humanobserver performance successfully.