Objective:To explore the dosimetry optimization strategy based on filter thickness and shape selection for the bulb superficial X-ray radiotherapy unit.Methods:Monte Carlo code TOPAS was used to model tubular equipment, and the dose distribution from six X-ray energies (50-150 kV) and five conventional aluminum filters (0.5-3.0 mm) with different thickness were simulated in the water model. The percentage depth dose (PDD) curve along the central axis, the center-axis profile dose at different depths, and the lateral dose distribution were analyzed. The dose distribution of three different designs of aluminum filters (conventional cylindrical, conical and oblique cylindrical filters) was compared to evaluate the effect of dosimetric optimization of different filter shapes.Results:Under the same energy, increasing the thickness of the filter can optimize the superficial skin dose, and the optimization effect of depth dose uniformity can be increased by 26% at a depth of 5.5 mm at 70 kV energy. The raised, flat and inclined dose distribution modes can be achieved by using conventional cylindrical, conical and inclined aluminum filters.Conclusions:By selecting the appropriate X-ray energy and filter thickness, an ideal dose distribution matching the tumor depth can be achieved. The application of personalized filters is also of great significance for diverse target areas.

Objective:To investigate the application of the conjugate gradient (CG) algorithm to treatment plan optimization for intensity-modulated brachytherapy (IMBT).Methods:The general Monte Carlo software TOPAS was utilized to simulate the 192Ir source of IMBT, and the unit dose contribution matrix was calculated. An objective function was established using the weighted least squares method and was solved using the CG algorithm to achieve optimized IMBT treatment plans. The optimization was validated using five clinical cervical cancer cases under modulation width 60°. The dose distributions of IMBT treatment plans under 45°, 60°, 90°, 120°, and 180° modulation widths were compared using the Wilcoxon test to determine the optimal IMBT treatment plan for cervical cancer treatment. Results:The CG algorithm successfully optimized IMBT treatment plans under modulation width 60° for five cases within 22.2 s on average. On the premise of sufficient target dose coverage, the average D2 cm 3 values of the bladder and rectum in IMBT treatment plans were 3.66 and 1.97 Gy, respectively, representing reductions of 0.54 and 0.69 Gy compared to traditional brachytherapy plans. For the five modulation widths, the D90% values of all IMBT treatment plans reached 6 Gy, without statistically significant differences ( P > 0.05). The average D2 cm 3 values of the bladder in IMBT treatment plans were significantly lower than those in the traditional brachytherapy plans( P<0.05), with modulation width 60° associated with the greatest reduction of 0.61 Gy. In contrast, the average D2 cm 3 values of the rectum under 45°, 60°, and 90° modulation widths decreased by 0.63, 0.54, and 0.45 Gy, respectively, compared to traditional plans, with statistically significant differences( P<0.05). Conclusions:The CG method enables rapid achievement of optimized IMBT treatment plans that meet clinical requirements, and modulation width 60° contributes to valid dosimetric optimization. This study can serve as a guide for the clinical implementation of IMBT.

Transmission X-ray tubes are relatively new devices characterized by portability,suitability for miniaturization,and low requirements for shielding,making them ideal radiation sources for kilovoltage X-ray therapy.However,their application in radiotherapy remains underexplored.An electron target model of a transmission X-ray tube is developed using the Monte Carlo toolkit TOPAS 3.8.1.The study investigates the effect of tungsten target thickness on X-ray output efficiency,finding that a tube voltage of 50 kV and a tungsten thickness of 1.4 μm yields the highest emission efficiency.Based on the energy spectrum at this optimal efficiency,polynomial fitting approach is applied to determine the corresponding aluminum filter thickness for mean energies ranging from 20 keV to 35 keV,achieving a mean fitting error of 0.91%.Next,the study simulates dose deposition in a water phantom for spectra with different mean energies and various source-to-surface distances,and plots percent-depth-dose curves,relative normalized dose-depth curves,and relative normalized dose histograms under each treatment condition.Finally,the simulated results are compared with experimental data from the intraoperative radiotherapy system Intrabeam and the superficial X-ray therapy unit SRT-100,obtaining average relative errors of 3.71%and 4.38%,respectively.These findings provide a theoretical foundation for further optimization of transmission X-ray tubes in kilovoltage radiotherapy.

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.

Transmission X-ray tubes are relatively new devices characterized by portability,suitability for miniaturization,and low requirements for shielding,making them ideal radiation sources for kilovoltage X-ray therapy.However,their application in radiotherapy remains underexplored.An electron target model of a transmission X-ray tube is developed using the Monte Carlo toolkit TOPAS 3.8.1.The study investigates the effect of tungsten target thickness on X-ray output efficiency,finding that a tube voltage of 50 kV and a tungsten thickness of 1.4 μm yields the highest emission efficiency.Based on the energy spectrum at this optimal efficiency,polynomial fitting approach is applied to determine the corresponding aluminum filter thickness for mean energies ranging from 20 keV to 35 keV,achieving a mean fitting error of 0.91%.Next,the study simulates dose deposition in a water phantom for spectra with different mean energies and various source-to-surface distances,and plots percent-depth-dose curves,relative normalized dose-depth curves,and relative normalized dose histograms under each treatment condition.Finally,the simulated results are compared with experimental data from the intraoperative radiotherapy system Intrabeam and the superficial X-ray therapy unit SRT-100,obtaining average relative errors of 3.71%and 4.38%,respectively.These findings provide a theoretical foundation for further optimization of transmission X-ray tubes in kilovoltage radiotherapy.

Objective:To explore the dosimetry optimization strategy based on filter thickness and shape selection for the bulb superficial X-ray radiotherapy unit.Methods:Monte Carlo code TOPAS was used to model tubular equipment, and the dose distribution from six X-ray energies (50-150 kV) and five conventional aluminum filters (0.5-3.0 mm) with different thickness were simulated in the water model. The percentage depth dose (PDD) curve along the central axis, the center-axis profile dose at different depths, and the lateral dose distribution were analyzed. The dose distribution of three different designs of aluminum filters (conventional cylindrical, conical and oblique cylindrical filters) was compared to evaluate the effect of dosimetric optimization of different filter shapes.Results:Under the same energy, increasing the thickness of the filter can optimize the superficial skin dose, and the optimization effect of depth dose uniformity can be increased by 26% at a depth of 5.5 mm at 70 kV energy. The raised, flat and inclined dose distribution modes can be achieved by using conventional cylindrical, conical and inclined aluminum filters.Conclusions:By selecting the appropriate X-ray energy and filter thickness, an ideal dose distribution matching the tumor depth can be achieved. The application of personalized filters is also of great significance for diverse target areas.

Objective:To investigate the application of the conjugate gradient (CG) algorithm to treatment plan optimization for intensity-modulated brachytherapy (IMBT).Methods:The general Monte Carlo software TOPAS was utilized to simulate the 192Ir source of IMBT, and the unit dose contribution matrix was calculated. An objective function was established using the weighted least squares method and was solved using the CG algorithm to achieve optimized IMBT treatment plans. The optimization was validated using five clinical cervical cancer cases under modulation width 60°. The dose distributions of IMBT treatment plans under 45°, 60°, 90°, 120°, and 180° modulation widths were compared using the Wilcoxon test to determine the optimal IMBT treatment plan for cervical cancer treatment. Results:The CG algorithm successfully optimized IMBT treatment plans under modulation width 60° for five cases within 22.2 s on average. On the premise of sufficient target dose coverage, the average D2 cm 3 values of the bladder and rectum in IMBT treatment plans were 3.66 and 1.97 Gy, respectively, representing reductions of 0.54 and 0.69 Gy compared to traditional brachytherapy plans. For the five modulation widths, the D90% values of all IMBT treatment plans reached 6 Gy, without statistically significant differences ( P > 0.05). The average D2 cm 3 values of the bladder in IMBT treatment plans were significantly lower than those in the traditional brachytherapy plans( P<0.05), with modulation width 60° associated with the greatest reduction of 0.61 Gy. In contrast, the average D2 cm 3 values of the rectum under 45°, 60°, and 90° modulation widths decreased by 0.63, 0.54, and 0.45 Gy, respectively, compared to traditional plans, with statistically significant differences( P<0.05). Conclusions:The CG method enables rapid achievement of optimized IMBT treatment plans that meet clinical requirements, and modulation width 60° contributes to valid dosimetric optimization. This study can serve as a guide for the clinical implementation of IMBT.

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.

Objective To develop a rapid and accurate Monte Carlo program(simplified code for dosimetry of carbon ions,SPEEDO)for carbon ion therapy.Methods For electromagnetic process,type Ⅱ condensed history simulation scheme and continuous slowing down approximation were used to simulate energy straggling,range straggling,multiple scattering,and ionization processes.For nuclear interaction,5 types of target nuclei were considered,including hydrogen,carbon,nitrogen,oxygen,and calcium.The produced secondary charged particles followed the same condensed history framework.The study simulated the transport of carbon ions in 4 materials(water,soft tissues,lung,and bone),and the calculated doses were validated against TOPAS(a Monte Carlo simulation software for radiotherapy physics),followed by a comparison with dose measurements in a water phantom from the HIMM-WW(a medical heavy-ion accelerator facility in Wuwei).Results SPEEDO's simulation results showed good consistency with TOPAS.For each material,in the voxel region where the physical dose was greater than 10％of the maximum dose point,the relative maximum dose error of both was less than 2％.At treatment energy of 400 MeV/u,SPEEDO's computation time was significantly less than that of TOPAS(13.8 min vs 105.0 min).SPEEDO's calculation results also showed good agreement with HIMM-WW measurements in terms of lateral dose distribution and integrated dose depth curve.Conclusion SPEEDO program can accurately and rapidly perform Monte Carlo dose calculations for carbon-ion therapy.

Objective To develop a GPU-based Monte Carlo dose calculation module for helical tomotherapy(TOMO),and integrate it into the commercial software ArcherQA to achieve fast and accurate dose verification in clinic.Methods The TOMO treatment head was modeled using TOPAS to obtain phase space files,and a fast weight tuning algorithm was used to simulate particle transport in multi-leaf collimator for improving computational efficiency,and finally,GPU-based Monte Carlo algorithms in ArcherQA were used to simulate particle transport in patients.To verify the model accuracy,the ArcherQA calculated results in water tank were compared with measured data for different open fields.In addition,multiple comparisons among ArcherQA results,TPS results and ArcCHECK results were conducted on 15 clinical cases(5 cases in the head and neck,5 cases in the chest and abdomen,and 5 cases in the whole body).Results In the water tank tests for 40 cm×5.0 cm,40 cm×2.5 cm and 40 cm× 1.0 cm radiation fields,the average global relative errors of the percentage depth dose,transverse dose distribution,and longitudinal dose distribution calculated by ArcherQA with the corresponding measured values were 0.72％,0.66％,and 0.54％,respectively.Over 98％of the voxels had a global relative error of less than 1％.As for 15 clinical cases,in 2％/2 mm criteria,the mean Gamma passing rate was 98.1％between ArcherQA and TPS,99.1％between TPS and ArcCHECK,and 99.4％between ArcherQA and ArcCHECK.The uncertainty of the simulation maintained less than 1％,and the average time taken for calculation based on patient CT vs ArcCHECK phantom was 87 s vs 64 s.Conclusion ArcherQA can be used for independent dose validation for TOMO plans for it can provide fast and accurate dose calculations.