Effect of anatomical noise on the detectability of cone beam CT images with different slice direction, slice thickness, and volume glandular fraction.

We investigate the effect of anatomical noise on the detectability of cone beam CT (CBCT) images with different slice directions, slice thicknesses, and volume glandular fractions (VGFs). Anatomical noise is generated using a power law spectrum of breast anatomy, and spherical objects with diameters from 1mm to 11mm are used as breast masses. CBCT projection images are simulated and reconstructed using the FDK algorithm. A channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) channels is used to evaluate detectability for the signal-known-exactly (SKE) binary detection task. Detectability is calculated for various slice thicknesses in the transverse and longitudinal planes for 15%, 30% and 60% VGFs. The optimal slice thicknesses that maximize the detectability of the objects are determined. The results show that the β value increases as the slice thickness increases, but that thicker slices yield higher detectability in the transverse and longitudinal planes, except for the case of a 1mm diameter spherical object. It is also shown that the longitudinal plane with a 0.1mm slice thickness provides higher detectability than the transverse plane, despite its higher β value. With optimal slice thicknesses, the longitudinal plane exhibits better detectability for all VGFs and spherical objects.