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 rapidly and accurately detect volumetric modulated arc therapy (VMAT) plans with potentially inaccurate radiation doses.Methods:The measurement-based dosimetric verification result of 196 VMAT plans obtained using ArcCHECK phantoms were retrospectively collected. Independent dosimetric calculation and verification were conducted for these plans using the ArcherQA system based on a fast Monte Carlo algorithm. The gamma passing rates of dosimetric verification using ArcCHECK phantom and the ArcherQA system were compared, followed by their correlation analysis and linear regression fitting. The ArcherQA system′s gamma passing rate threshold used to detect positive dosimetric verification result obtained using ArcCHECK phantoms, as well as the specificity of the detection, were calculated. Based on this gamma passing rate threshold, another 50 VMAT plans were selected as a test set to assess the ArcherQA system′s ability to detect positive measurement-based dosimetric verification result.Results:The average gamma passing rates for the dosimetric verification of the VMAT plans using the ArcherQA system and ArcCHECK phantoms were 97.28% and 96.57% (3%/3 mm, TH=10%), respectively. Both rates had a correlation coefficient of 0.71 ( P < 0.01) and a linear fitting coefficient of 0.54 ( R2=0.51). When the gamma passing rate for dosimetric verification using ArcCHECK phantoms was set at 90% (3%/2 mm, TH=10%), the gamma passing rate threshold for dosimetric verification using the ArcherQA system should be adjusted to 94.8% to detect all VMAT plans with positive dosimetric verification result obtained using ArcCHECK phantoms, with a specificity of 67.8%. Using this threshold, the ArcherQA system detected all VMAT plans in the test set for which ArcCHECK phantom-based measurement yielded positive dosimetric verification result. Conclusions:By determining an appropriate gamma passing rate threshold, the ArcherQA system can rapidly and accurately detect VMAT plans with potentially inaccurate doses, thus ensuring treatment accuracy and improving work efficiency.

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 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 rapidly and accurately detect volumetric modulated arc therapy (VMAT) plans with potentially inaccurate radiation doses.Methods:The measurement-based dosimetric verification result of 196 VMAT plans obtained using ArcCHECK phantoms were retrospectively collected. Independent dosimetric calculation and verification were conducted for these plans using the ArcherQA system based on a fast Monte Carlo algorithm. The gamma passing rates of dosimetric verification using ArcCHECK phantom and the ArcherQA system were compared, followed by their correlation analysis and linear regression fitting. The ArcherQA system′s gamma passing rate threshold used to detect positive dosimetric verification result obtained using ArcCHECK phantoms, as well as the specificity of the detection, were calculated. Based on this gamma passing rate threshold, another 50 VMAT plans were selected as a test set to assess the ArcherQA system′s ability to detect positive measurement-based dosimetric verification result.Results:The average gamma passing rates for the dosimetric verification of the VMAT plans using the ArcherQA system and ArcCHECK phantoms were 97.28% and 96.57% (3%/3 mm, TH=10%), respectively. Both rates had a correlation coefficient of 0.71 ( P < 0.01) and a linear fitting coefficient of 0.54 ( R2=0.51). When the gamma passing rate for dosimetric verification using ArcCHECK phantoms was set at 90% (3%/2 mm, TH=10%), the gamma passing rate threshold for dosimetric verification using the ArcherQA system should be adjusted to 94.8% to detect all VMAT plans with positive dosimetric verification result obtained using ArcCHECK phantoms, with a specificity of 67.8%. Using this threshold, the ArcherQA system detected all VMAT plans in the test set for which ArcCHECK phantom-based measurement yielded positive dosimetric verification result. Conclusions:By determining an appropriate gamma passing rate threshold, the ArcherQA system can rapidly and accurately detect VMAT plans with potentially inaccurate doses, thus ensuring treatment accuracy and improving work efficiency.

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 investigate the values of two-dimensional and three-dimensional echocardiographic parameters in predicting pulmonary vascular resistance (PVR) in chronic pulmonary thromboembolic pulmonary hypertension (CTEPH).Methods:A total of 141 patients diagnosed with CTEPH in China-Japan Friendship Hospital from November 2015 to December 2022 were included. Two-dimensional echocardiographic indicators reflecting PVR were constructed according to the calculation formula of PVR: echocardiographic estimated systolic pulmonary artery pressure (sPAP Echo)/left ventricular end-diastolic diameter (LVIDd), echocardiographic estimated mean pulmonary artery pressure (mPAP Echo)/LVIDd. sPAP Echo/left ventricular end-diastolic volume (LVEDV), sPAP Echo/left ventricular cardiac output (LVCO) were measured by three-dimensional echocardiography. The correlations between two-dimensional and three-dimensional echocardiographic ratios and invasive PVR were then analyzed using the Spearman correlation method. Using receiver operating characteristic curve analysis, cut-off values for the ratios were generated to identify patients with PVR>1 000 dyn·s -1·cm -5. Pre- and postoperative hemodynamics and echocardiographic data were analyzed, as well as the correlation between the reduction rate of the echocardiographic index and PVR in 54 patients who underwent pulmonary endarterectomy (PEA). Results:sPAP Echo/LVIDd, sPAP Echo/LVEDV and sPAP Echo/LVCO were moderately correlated with PVR( rs=0.62, 0.52, 0.63, both P<0.001). The ratio of sPAP Echo to LVEDV, when greater than or equal to 1.41, had a sensitivity of 0.800 and a specificity of 0.930 for determining PVR >1 000 dyn·s -1·cm -5 (AUC=0.860, P<0.001). Similarly, the ratio of sPAP Echo to LVIDd, when greater than or equal to 2.14, had a sensitivity of 0.647 and a specificity of 0.861 for determining PVR >1000 dyn·s -1·cm -5 (AUC=0.830, P<0.001). The sPAP Echo/LVIDd and mPAP Echo/LVIDd significantly decreased after PEA (both P<0.001). The sPAP Echo/LVIDd and mPAP Echo/LVIDd reduction rate (ΔsPAP Echo/LVIDd and ΔmPAP Echo/LVIDd) were significantly correlated with PVR reduction rate (ΔPVR), respectively ( rs=0.61, 0.63, both P<0.05). Conclusions:Two-dimensional ratio sPAP Echo/LVIDd and three-dimensional ratio sPAP Echo/LVEDV can be used to noninvasively estimate PVR in CTEPH patients. The conventional ratio sPAP Echo/LVIDd is convenient and reproducibly suitable for monitoring the improvement of PVR before and after treatment, and its ratio of 2.14 can predict the significant increase of PVR in CTEPH patients (>1 000 dyn·s -1·cm -5).

Objective:To develop a dose prediction-based quantitative evaluation method of the quality of radiotherapy plans, and to verify the clinical feasibility and clinical value of the method .Methods:The 3D U-Netwas trained using the radiotherapy plans of 45 rectal cancer cases that were formulated by physicists with more than five years of radiotherapy experience. After obtaining 3D dose distribution using 3D U-Net prediction, this study established the plan quality metrics of intensity modulated radiotherapy(IMRT) rectal cancer radiotherapy plans using dose-volume histogram(DVH) indexes of dose prediction. Then, the initial scores of rectal cancer radiotherapy plans were determined.Taking the predicted dose as the optimization goal, the radiotherapy plans were optimized and scored again. The clinical significance of this scoring method was verified by comparing the scores and dosimetric parameters of the 15 rectal cancer cases before and after optimization.Results:The radiotherapy plans before and after optimization all met the clinical dose requirements. The total scores were(77.21±9.74) before optimization, and (88.78±4.92) after optimization. Therefore, the optimized radiotherapy planswon increased scores with a statistically significant difference( t=-4.105, P<0.05). Compared to the plans before optimization, the optimized plans show decreased Dmax of all organs at risk to different extents. Moreover, the Dmax, V107%, and HI of PTV and the Dmax of the bladder decreased in the optimized plans, with statistically significant differences ( t=2.346-5.771, P<0.05). There was no statistically significant difference in other indexes before and after optimization ( P>0.05).The quality of the optimized plans were improved to a certain extent. Conclusions:This study proposed a dose prediction-based quantitative evaluation method of the quality of radiotherapy plans. It can be used for the effective personalized elevation of the quality of radiotherapy plans, which is beneficial to effectively compare and review the quality of clinical plans determined by different physicists and provide personalized dose indicators. Moreover, it can provide great guidance for the formulation of clinical therapy plans.

Objective:To develope a self-adjustable automatic planning method of intensity modulated radiotherapy based on predicted dose, in order to enhance the robustness of automatic planning.Methods:After the patients′ dose by 3D U-Res-Net_B network was predicted, the current dose was calculated based on the last iteration result, then the predicted dose was combined to calculate the target dose and optimized. With all iterations completed or exit conditions satisfied, final treatment plannings would be acquired. A total of 30 cases of rectal cancer were tested to verify the effectiveness of the algorithm.Results:The mean value of planning target volumes′ V100% was (95.03±0.91)% for clinical plans, close to (94.67±1.96)% for automatical plans( P>0.05), and better than (92.90±2.13)% for predicted dose with the statisically significant difference ( t=29.0, P<0.05). Automatic planning′s indexes such as V35 of small intestines, V40 of bladders and V20 - V40 of femoral heads were lower than predicted and clinical ones, with the statisically significant difference( t=4.5-118.0, P<0.05). Discrepancy in other indexes of organs at risk was not statistically significantly different( P>0.05). Conclusions:This method made automatic planning processes more robust and more adaptive to difficult clinical situations.

Objective To evaluate the feasibility of utilizing dose-volume histogram (DVH) prediction models of organs at risk (OARs) to deliver automatic treatment planning of prostate cancer.Methods The training set included 30 cases randomly selected from a database of 42 cases of prostate cancer receiving treatment planning.The bladder and rectum were divided into sub-volumes (Ai) of 3 mm in layer thickness according to the spatial distance from the boundary of planning target volume (PTV).A skewed normal Gaussian function was adopted to fit the differential DVH of Ai,and a precise mathematical model was built after optimization.Using the embedded C++ subroutine of Pinnacle scripa,ahe volume of each Ai of the remaining validation set for 12 patients was obtained to predict the DVH parameters of these OARa,ahich were used as the objective functions to create personalized Pinnacle script.Finalla,automatic plans were generated using the script.The dosimetric differences among the original clinical plannina,aredicted value and the automatic treatment planning were statistically compared with paired t-test.Results DVH residual analysis demonstrated that predictive volume fraction of the bladder and rectum above 6 000 cGy were lower than those of the original clinical planning.The automatic treatment planning significantly reduced the V70,V60,V50 of the bladder and the V70 and V60 of the rectum than the original clinical planning (all P＜0.05),the coverage and conformal index (CI) of PTV remained unchangea,and the homogeneity index (HI) was slightly decreased with no statistical significance (P＞ 0.05).Conclusion The automatic treatment planning of the prostate cancer based on the DVH prediction models can reduce the irradiation dose of OARs and improve the treatment planning efficiency.