Modelling small block aperture in an in-house developed GPU-accelerated Monte Carlo-based dose engine for pencil beam scanning proton therapy

Purpose: To enhance an in-house graphic-processing-unit (GPU) accelerated virtual particle (VP)-based Monte Carlo (MC) proton dose engine (VPMC) to model aperture blocks in both dose calculation and optimization for pencil beam scanning proton therapy (PBSPT)-based stereotactic radiosurgery (SRS). Methods and Materials: A block aperture module was integrated into VPMC. VPMC was validated by an opensource code, MCsquare, in eight water phantom simulations with 3cm thick brass apertures: four were with aperture openings of 1, 2, 3, and 4cm without a range shifter, while the other four were with same aperture opening configurations with a range shifter of 45mm water equivalent thickness. VPMC was benchmarked with MCsquare and RayStation MC for 10 patients with small targets (average volume 8.4 cc). Finally, 3 patients were selected for robust optimization with aperture blocks using VPMC. Results: In the water phantoms, 3D gamma passing rate (2%/2mm/10%) between VPMC and MCsquare were 99.71$\pm$0.23%. In the patient geometries, 3D gamma passing rates (3%/2mm/10%) between VPMC/MCsquare and RayStation MC were 97.79$\pm$2.21%/97.78$\pm$1.97%, respectively. The calculation time was greatly decreased from 112.45$\pm$114.08 seconds (MCsquare) to 8.20$\pm$6.42 seconds (VPMC), both having statistical uncertainties of about 0.5%. The robustly optimized plans met all the dose-volume-constraints (DVCs) for the targets and OARs per our institutional protocols. The mean calculation time for 13 influence matrices in robust optimization by VPMC was 41.6 seconds. Conclusion: VPMC has been successfully enhanced to model aperture blocks in dose calculation and optimization for the PBSPT-based SRS.Comment: 3 tables, 3 figure

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy

Purpose: To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. Methods: PBSPT plans of 103 prostate cancer patients and 83 lung cancer patients previously treated at our institution were included in the study, each with CTs, structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine. For the ablation study, we designed three experiments corresponding to the following three methods: 1) Experiment 1, the conventional region of interest (ROI) method. 2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. 3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. Results: Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices. For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method can improve the passing rates in these regions and the sliding window method further improved them. A similar trend was also observed for the dice coefficients. In fact, this trend was especially remarkable for relatively low prescription isodose lines. The dose predictions for all the testing cases were completed within 0.25s

Long term aging of Selenide glasses: Evidence of sub-Tg endotherms and pre-Tg exotherms

Long term aging is studied on several families of chalcogenide glasses including the Ge-Se, As-Se, Ge-P-Se and Ge-As-Se systems. Special attention is given to the As-Se binary, a system that displays a rich variety of aging behavior intimately tied to sample synthesis conditions and the ambient environment in which samples are aged. Calorimetric (Modulated DSC) and Raman scattering experiments are undertaken. Our results show all samples display a sub-Tg endotherm below Tg in glassy networks possessing a mean coordination number r in the 2.25 < r < 2.45 range. Two sets of AsxSe1-x samples aged for 8 years were compared, set A consisted of slow cooled samples aged in the dark, and set B consisted of melt quenched samples aged at laboratory environment. Samples of set B in the As concentration range, 35% < x < 60%, display a pre-Tg exotherm, but the feature is not observed in samples of set A. The aging behavior of set A presumably represents intrinsic aging in these glasses, while that of set B is extrinsic due to presence of light. The reversibility window persists in both sets of samples but is less well defined in set B. These findings contrast with a recent study by Golovchak et al., which finds the onset of the reversibility window moved up to the stoichiometric composition (x = 40%). Here we show that the upshifted window is better understood as resulting due to demixing of As4Se4 and As4Se3 molecules from the backbone, i.e., Nanoscale phase separation (NSPS). We attribute sub-Tg endotherms to compaction of the flexible part of networks upon long term aging, while the pre-Tg exotherm to NSPS. Finally, the narrowing and sharpening of the reversibility window upon aging is interpreted as the slow 'self-organizing' stress relaxation of the phases just outside the Intermediate phase.Comment: In press - J. of Physics: Condensed Matte