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

Microcalcification (μCalc) detection plays an important role in breast cancer screening. Electronic noise in energy-integrating detectors (EID) 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. Approach. We employed VICTRE toolkit to generate a realistic breast phantom with a 0.25 mm³ voxel 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. 360 projections were simulated with a total number of 3.15×10¹² photons, 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 μCalcs 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. bCBCT images were reconstructed with the Feldkamp-Davis-Kress algorithm, and image quality and μCalc detection performance were evaluated. Main results. 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⁻¹ for EID-bCBCT and 0.038~cm⁻¹ for PCD-bCBCT, respectively. μCalcs 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 for 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 μCalc sizes. Significance. We compared EID- and PCD-based bCBCT for μCalc detection using GPU-based MC simulations in a clinically realistic setting. Our results demonstrate a potential advantage of PCD-bCBCT for this detection task.