Online proton range verification is a rapidly emerging field characterised by its ability to reduce the error margins during proton beam therapy, as it is patient-specific and in vivo. In particular, secondary prompt gamma detection is a promising tool to monitor the dose delivery. The present research evaluates the capability of a HEXITEC detector to identify the prompt gammas produced during proton beam therapy, and assesses its potential for online range verification. To achieve this, the detector is placed at one side of a water phantom, which is irradiated at different proton energies in the University College London Hospital Proton Centre. For further analysis, Monte Carlo simulations are performed using Geant4 and the same geometry as the experiment. The results show that HEXITEC has the potential to be part of a detection system that could identify secondary prompt gammas within the secondary field produced inside the target, allowing for the in-detector discrimination of these particles via cluster size analysis. The comparison between data sets shows that there is a high level of accuracy between the model and the experimental measurements in terms of secondary flux and charge diffusion inside the detector, which poses the model as a fundamental tool for future optimisation studies.

Objective: To systematically assess the accuracy and computational performance of a newly proposed stochastic differential equation (SDE)-based model for proton beam dose calculation by benchmarking it against Geant4 in a set of simplified but increasingly challenging phantom geometries. Approach: Building on previous work in Crossley et al. (2025), where energy deposition from a proton beam was modelled using an SDE framework, we implemented the model using standard approximations to interaction cross sections and mean excitation energies, enabling straightforward adaptation to new materials and configurations. The model was benchmarked against Geant4 in homogeneous, longitudinally heterogeneous and laterally heterogeneous phantoms, for assessment of depth-dose behaviour, lateral transport and impact of material heterogeneities. Main results: Across all phantom configurations and beam energies, the SDE model reproduced the main depth–dose characteristics predicted by Geant4, with proton range agreement within 0.2 mm for 100 MeV beams and within 0.6 mm for 150 MeV beams. Voxel-wise comparisons yielded gamma pass rates exceeding 95% for all cases under strict 2%/0.5 mm criteria with a 1% dose threshold. Differences between the two approaches were spatially localised and primarily associated with regions of steep dose gradients or material heterogeneities, while overall lateral beam dispersion was well reproduced. In terms of computational performance, the SDE model achieved speed-up factors of approximately 2.5–3 relative to single-threaded Geant4, consistently across different Geant4 physics lists. Significance: These results demonstrate that the SDE-based approach can reproduce key dosimetric features predicted by high-fidelity Monte Carlo simulations with good accuracy while already offering a moderate reduction in computational cost. Owing to its formulation, the method is naturally amenable to parallel and GPU-accelerated implementations, suggesting potential for substantial further speed improvements. This makes the approach a promising candidate for fast dose calculations in proton therapy.

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.