To evaluate the feasibility of delivering minibeam radiotherapy (MBRT) to a canine glioblastoma patient using a novel low-cost dual-robot kilovoltage (kV) x-ray system. 
 
Approach: A workflow to perform MBRT using a tungsten collimator with 300 μm slits was developed for the 225 kVp x-ray source system. A Monte Carlo (MC) model of MBRT was built in TOPAS and validated with EBT4 measurements in-air and in a solid water phantom. A non-coplanar MBRT treatment plan with 2 mm Al- and 0.5 mm Cu-filtered beams was created for the canine patient and compared to a clinical 6 MV VMAT plan. A full set and individual minibeam fields were delivered to a canine head phantom with an intracranial film to verify plan feasibility.

Main results: Measured and MC-simulated peak-to-valley dose ratios in-air and in the water phantom agreed within ≤ 2σ. Open-beam and collimated peak dose measurements at depths acquired with films in the water phantom agreed with simulations with mean relative differences of 1% and 2%, respectively. MBRT treatment plan spatial fractionation was preserved in healthy tissue for tissue-sparing benefits. Mean MBRT doses to organs at risk were within 15% of VMAT, except for skin (+52% for Al- and +48% for Cu-filtered beams), bone (+28% for Al and +22% for Cu) and the right ear (-39% for Al and -42% for Cu). Dose difference map average in the canine head phantom was 2.3 ± 1.2% for one and 3.5 ± 2.2% for two minibeam fields. 

Significance: These results demonstrate the feasibility of MBRT delivery using the novel x-ray system to a canine due to spatial fractionation and adequate depth dose delivered to the tumor.

Optical-photon Monte Carlo (MC) underlies scintillator dosimetry, Cherenkov imaging and other medical-physics applications, yet Geant4 transport of the 105-106optical photons per event on the central processing unit (CPU) makes dataset-scale studies impractical even on a multi-core workstation. We implement and verify a hardware ray-tracing optical-photon transport extension for TOPAS on Apple Silicon.
Approach. Charged-particle transport remains in Geant4 on the CPU, where the extension computes the scintillation and Cherenkov yields. Optical-photon generation, propagation and scoring run on the graphics processing unit (GPU), which finds boundaries with its ray-tracing hardware or, for curved solids, by solving the surface equation. The optical processes follow their Geant4 counterparts, with the dielectric-dielectric boundary physics algebraically identical. We verify the extension with ten benchmarks (B1-B10), covering optical physics, detector-chain timing and curved-surface intersection, on an Apple M3 Ultra with an 80-core GPU and a 30-thread CPU baseline.
Main results. Every verification endpoint passes the equivalence test declared in advance, with margins of ±1 % in fluence and ±1 percentage point in the reflection and transmission probabilities. The largest mean summed-fluence deviation is 0.147 %, and every summed-fluence 95 % confidence interval lies within ±1 %. The scintillation-reflector-wavelength-shifting chain agrees to -0.02 % and the curved-geometry imaging chain to -0.07 %, and the intersection study reproduces the geometric chords of whole solids within a 0.5 mm tolerance. The matched scintillator-lens-camera simulation runs 15.7-32.0 times faster in execution time and about 24 times faster end to end at 105events.
Significance. The extension reproduces the analytical or CPU-Geant4 reference process by process within these margins, while accelerating matched transport by more than an order of magnitude. TOPAS scripts gain GPU acceleration without user-written C++, enabling dataset-scale generation of scintillation-dosimetry training sets and Cherenkov-imaging forward models on a single workstation.&#xD.

Objective
Magnetic resonance-integrated proton therapy (MRiPT) offers the potential for high-precision, image-guided particle therapy but is challenged by magnetic field (MF)-induced proton beam deflection and radiation field deformation. Unless compensated for, these effects compromise dose conformity and prohibit clinical implementation. This work aims to develop and experimentally validate a spot-wise compensation method capable of restoring proton dose distributions in the presence of a measured 3D magnetic vector field map within a clinical treatment planning system (TPS).
Approach
A spot-wise compensation framework was implemented in a research version of RayStation, a TPS commercially available for clinical use. The method compensates both lateral beam deflection and MF-induced radiation field deformation by shifting individual pencil-beam spots based on displacement vectors derived from dose calculations in water phantoms using the measured MF of an in-beam MR scanner integrated with a horizontal proton beamline. Two complementary strategies were implemented: a patient-specific approach based on plan-specific displacement calculations, and a generic approach using precomputed displacement maps derived from a reference phantom. Compensation performance was evaluated in representative clinical cases. Dose distributions were experimentally validated using 2D ionisation chamber array measurements.
Main Results
Both compensation methods effectively restored target coverage, dose homogeneity, and organ-at-risk sparing to the level of no-MF reference plans. The patient-specific method generally achieved a higher performance, whereas the generic compensation method enabled rapid automated correction with sufficient accuracy for clinical workflows involving deep-seated tumours. Local γ-analysis demonstrated excellent agreement between measured and calculated doses, with pass rates exceeding 98.5% for 3%/3 mm criteria.
Significance
This work demonstrates that compensation of MF-induced proton beam deflection and field deformation can be achieved entirely within a research version of the RayStation TPS using measured, MR scanner-specific MF data. The proposed method provides a scalable, robust framework for future MRiPT clinical applications.&#xD.

Abstract
Objective. To develop an automated method for segmentation and uptake quantification of
carotid atherosclerotic inflammation on 64Cu-CANF-comb PET while accounting for
partial-volume and tissue-fraction effects.
Approach. Synthetic carotid PET images with known fractional-volume ground truth were
generated by combining MR-derived high-resolution activity maps with GATE Monte
Carlo simulation incorporating major PET physical processes and matched to the
Biograph mMR acquisition and reconstruction. A two-stage network first localized the
carotid vessel wall from T1-weighted MR and then estimated voxel-wise inflammation
fraction from masked PET. Both stages used a multiscale residual (MSR) convolutional
backbone. Performance was compared with U-Net, Transformer U-Net, DUCK-Net, and
iterative Yang partial-volume correction.
Main results. In a two-alternative forced-choice study, five nuclear-medicine physicians
identified real images with 54% accuracy (54/100; Wilson 95% CI 0.44-0.63; exact
binomial p = 0.48). On 184 simulated test slices from five held-out patients, the proposed
method achieved a weighted DSC of 0.79, compared with 0.73 for U-Net, 0.69 for
Transformer U-Net, and 0.75 for DUCK-Net, with corresponding HD values of 1.85, 2.29,
2.32, and 2.23 voxels. Patient-cluster bootstrap comparisons favored the proposed method
for DSC and HD against all three baselines (Holm-corrected p ≤ 0.031). Overall ENRMSE
was 0.24 versus 0.59, 0.68, and 0.59 for the three DL baselines and 0.57 for iterative Yang.
In seven patients, corrected SUV showed a positive association with MR-derived
lipid-rich/necrotic core with hemorrhage.
Significance. The proposed framework improved carotid PET segmentation and uptake
quantification under simulated conditions. The patient analysis is preliminary and requires
external validation with biological reference standards before clinical deployment.

In the context of radiological accidents, retrospective dosimetry methods such as electron paramagnetic resonance (EPR) spectroscopy and Monte Carlo simulation are used for dose reconstruction. EPR analysis of bone biopsies allows experimental assessment of absorbed dose. However, several factors may affect EPR dose estimation. The time delay between irradiation and sampling can lead to underestimation of absorbed dose to bone, and the influence of beam quality needs investigation. Therefore, this study aims to evaluate the influence of X-ray beam quality on bone EPR signal kinetics and the absorbed dose distribution in bone.
Approach: A murine model of localized hind limb irradiation was developed using three beam qualities (50 kVp, 220 kVp, 10 MV). EPR measurements were performed to characterize radio-induced free radical kinetics in bone collected at different time points, from irradiation day to six months post-irradiation. A computational model of the irradiation setup was implemented in GEANT4 to study the dose distribution and complement the experimental observations.
Main results: High-energy X-rays (10 MV) led to a rapid decrease in EPR signal, with 75% signal loss after 21 days and no detectable signal under our conditions after 42 days. For low-energy X-rays, the signal decreased more rapidly at 220 kVp than at 50 kVp, with signal loss of 51% and 31% respectively at 21 days, and a residual EPR signal remains at later time points (70-80% signal loss at six months). Monte Carlo simulations highlighted heterogeneous dose deposition between tissues with low-energy X-rays, as well as significant beam attenuation within mouse tibia for 50 kVp X-rays, leading to inhomogeneous dose deposition in bone.
Significance: Using a preclinical model, we have demonstrated that X-ray beam quality and time affect bone dose estimation. These findings improve our understanding of how radiation quality and bone physiology affect outcomes over time after irradiation.

Radiomics analysis in PET offers a high-dimensional representation of tissue characteristics, yet its reliability is limited by sensitivity to acquisition perturbations, particularly dose reduction. This study aims to introduce a nonlinear stability phenotyping framework to characterize dose-response behavior of multi-organ radiomics and evaluate their potential relevance for future digital twin-oriented applications.
Approach: A multi-center dataset of 550 total-body PET scans was analyzed. Dose reduction was simulated using list-mode subsampling (dose reduction factor - DRF = 2-100). A total of 107 radiomics features were extracted per organ. Stability was quantified using Intraclass Correlation Coefficient (ICC) and summarized as stability fractions. Nonlinear models were fitted to organ- and feature-level trajectories. Stability phenotypes and critical thresholds were derived, along with organ-level instability metrics.
Main results: Radiomics stability exhibited strongly nonlinear, organ-dependent behavior. Feature-level analysis identified frequent sigmoid-associated collapse patterns with high descriptive model fits (R²>0.90). The liver showed the highest instability burden (30.8% unstable features), followed by esophagus and kidneys (18-20%). Texture features were the primary source of instability, whereas shape features remained robust. Organ-level trajectories demonstrated heterogeneous responses, with some organs maintaining high stability (0.80-0.95) across DRFs, while others showed sharp declines (down to 0.64). Linear (stable) behavior dominated at the organ level, though masking underlying nonlinear feature dynamics.
Significance: Radiomics stability in total-body PET follows nonlinear, phenotype-driven dynamics with identifiable tipping points and organ-specific sensitivity. The proposed framework provides a behavior-based approach for evaluating radiomics robustness and supports the selection of reliable features and organs for robust quantitative imaging and may inform future digital twin-oriented applications.