Comparing energy-integrating detector and photon-counting detector-based breast cone beam CTs for microcalcification detection via Monte Carlo simulation

<i>Objective.</i>Microcalcification (<i>µ</i>Calc) detection plays an important role in breast cancer screening. Electronic noise in energy-integrating detectors (EIDs) is the major challenge for this task in current breast cone-beam CT (bCBCT) due to the tight dose constraint for breast imaging. bCBCT with a photon counting detector (PCD) can potentially offer a higher spatial resolution and lower noise. This study performed a direct comparison of bCBCTs with the two detector types via GPU-based Monte Carlo (MC) simulation.<i>Approach.</i>We employed Virtual Clinical Trial for Regulatory Evaluation toolkit to generate a realistic breast phantom with a 0.25mm3voxel size, 80% fat fraction and 14 cm diameter. We considered a bCBCT system with a 60 kV x-ray source filtered with 0.3 mm Cu and detector response functions for PCD and EID. A total of 360 projections were simulated with a total number of3.15×1012photons, corresponding to ∼4 mGy mean glandular dose, comparable to a two-view mammography. We modified our GPU-based MC simulation code to incorporate analytical descriptions of<i>µ</i>Calcs of spherical shapes with diameters ranging from 0.1 to 0.4 mm, in 0.1 mm increments, into the voxelized phantom. A nichrome wire with 0.07 mm diameter was simulated to calculate the modulation transfer functions (MTFs). bCBCT images were reconstructed with the Feldkamp-Davis-Kress algorithm, and image quality and<i>µ</i>Calc detection performance were evaluated.<i>Main results.</i>EID-bCBCT had more profound image noise due to electronic noise. The image intensity standard deviations estimated within a region of interest were 0.055 cm^-1for EID-bCBCT and 0.038 cm^-1for PCD-bCBCT, respectively.<i>µ</i>Calcs and breast anatomy such as ligaments were more visible in the PCD-bCBCT images. The 10% MTF cutoffs were 5.5 and 9.5 lp mm^-1for EID-bCBCT and PCD-bCBCT, respectively. Contrast-to-noise ratio ranged in 1.20-9.13 for EID-bCBCT and 3.07-14.74 for PCD-bCBCT, depending on<i>µ</i>Calc sizes.<i>Significance.</i>We compared EID- and PCD-based bCBCT for<i>µ</i>Calc detection using GPU-based MC simulations in a clinically realistic setting. Our results demonstrate a potential advantage of PCD-bCBCT for this detection task.