X-ray imaging of dense breasts and breast implants often suffers from reduced lesion visibility because strong attenuation lowers contrast, while conventional rhodium (Rh) K-edge filtering suppresses part of the high-energy spectral tail. This study presents a Monte Carlo framework for spectroscopic mammography using a voxelated 1 mm thick cadmium telluride (CdTe) sensor and a first-order detector interaction model to evaluate energy-dependent image quality. The model reproduces fluorescence and inter-voxel energy redistribution in CdTe, but not the full detector chain, and remains idealized with respect to charge transport, carrier collection, threshold dispersion, and pile-up. Energy-resolved simulations in the 10–50 keV range were used to compute spectroscopic contrast-to-noise ratio (CNR) curves and to form integrated-spectrum (IS) images for four tested spectra. For the dense-breast calcium hydroxyapatite (HA) speck detection task considered here, and under the present simulation assumptions, replacing the standard 28 kVp + 50 μm Rh spectrum with 28 kVp + 1 mm Al increased the simulated IS image CNR by 23.11%, with an approximately 5% increase in estimated primary-incident air kerma at the phantom entrance plane. Preliminary experimental implant-phantom images were included as a qualitative feasibility check, showing a trend consistent with simulations. Within the limits of this task-specific simulation, the results suggest that preserving the transmitted high-energy tail can improve HA speck visibility for the present 1 mm CdTe photon-counting detector, with the 28 kVp + 1 mm Al spectrum outperforming the other tested cases.

X-ray imaging of dense breasts and breast implants often suffers from reduced lesion visibility due to strong attenuation, while conventional rhodium (Rh) K-edge filtering typically suppresses high-energy photons. This study presents a Monte Carlo-based simulation and optimization framework for spectroscopic mammography using a voxelated Cadmium Telluride (CdTe) sensor, enabling quantitative evaluation of energy-dependent image quality. The system accurately simulates sensor fluorescence and inter-voxel energy redistribution, enabling direct comparability with real-world performance. Energy-resolved simulations in the 10-50 keV range were used to compute spectroscopic contrast-to-noise ratio (CNR) curves and identify optimal spectral regions and filter configurations. Replacing the standard Rh filter with aluminum (Al) filtration increased CNR by more than 23% with only a ~5% increase in entrance surface dose (ESD), significantly improving the visibility of hydroxyapatite microcalcifications, even behind dense tissue or implants. The shown work demonstrates practical guidelines and analysis for energy-resolved imaging optimizations obtained from simulations. The results imply that spectroscopic photon-counting detectors and methods can enhance dense-breast mammography image quality while maintaining low patient dose.