Background and purposes: To compare the dosimetry for the left-sided breast cancer treatment using five different radiotherapy techniques. Materials and methods: Twenty patients with left sided breast cancer were treated with conservative surgery followed by radiotherapy. They were planned using five different radiotherapy techniques, including: 1) conventional tangential wedge-based fields (TW); 2) field-in-field (FIF) technique; 3) tangential inverse planning intensity-modulated radiation therapy (T-IMRT); 4) multi-field IMRT (M-IMRT); and 5) volumetric modulated arc therapy (VMAT). The CTV, PTV and OARs including the heart, the regions of coronary artery (CA), the contralateral breast, the left and right lung were delineated. The PTV dose was prescribed 50Gy and V_47.5≥95%. Same dose constraint was used for all five plans. The planned volumetric dose of PTV and PRV-OARs were compared and analyzed. Results: Except VMAT (Average V_47.5 was 94.72%±1.2%), all the other four plans were able to meet the V95% (V_47.5) requirement. T-IMRT plan improved the PTV dose homogeneity index (HI) by 0.02 and 0.03 when compared to TW plan and VMAT plan, and decreased the V₅, V₁₀ and V₂₀ of all PRV-OARs. However, the high dose volume (≥ 30Gy) of the PRV-OARs in T-IMRT plan had no statistically significant difference compared with the other two inverse plans. In all five plans, the dose volume of coronary artery area showed a strong correlation to the dose volume of the heart (the correlation coefficients were 0.993, 0.996, 1.000, 0.995 and 0.986 respectively). Conclusion: Compared to other techniques, the T-IMRT technology reduced radiation dose exposure to normal tissues and maintained reasonable target homogeneity, VMAT is not recommended for left-sided breast cancer treatment. In five techniques, the dose-volume histogram (DVH) of the heart can be used to predict the dose-volume histogram (DVH) of the coronary artery.

This study aims to utilize a deep convolutional neural network (DCNN) for synthesized CT image generation based on cone-beam CT (CBCT) and to apply the images to dose calculations for nasopharyngeal carcinoma (NPC). An encoder-decoder 2D U-Net neural network was produced. A total of 70 CBCT/CT paired images of NPC cancer patients were used for training (50), validation (10) and testing (10) datasets. The testing datasets were treated with the same prescription dose (70 Gy to PTVnx70, 68 Gy to PTVnd68, 62 Gy to the PTV62 and 54 Gy to the PTV54). The mean error (ME) and mean absolute error (MAE) for the true CT images were calculated for image quality evaluation of the synthesized CT. The dose-volume histogram (DVH) dose metric difference and 3D gamma pass rate for the true CT images were calculated for dose analysis, and the results were compared with those for the CBCT images (original CBCT images without any correction) and a patient-specific calibration (PSC) method. Compared with CBCT, the range of the MAE for synthesized CT images improved from (60, 120) to (6, 27) Hounsfield units (HU), and the ME improved from (−74, 51) to (−26, 4) HU. Compared with the true CT method, the average DVH dose metric differences for the CBCT, PSC and synthesized CT methods were 0.8% ± 1.9%, 0.4% ± 0.7% and 0.2% ± 0.6%, respectively. The 1%/1 mm gamma pass rates within the body for the CBCT, PSC and synthesized CT methods were 90.8% ± 6.2%, 94.1% ± 4.4% and 95.5% ± 1.6%, respectively, and the rates within the PTVnx70 were 80.3% ± 16.6%, 87.9% ± 19.7%, 98.6% ± 2.9%, respectively. The DCNN model can generate high-quality synthesized CT images from CBCT images and be used for accurate dose calculations for NPC patients. This finding has great significance for the clinical application of adaptive radiotherapy for NPC.

A method using both patient geometric and dosimetric information was proposed to predict dose-volume histograms (DVHs) of organs at risk (OARs) for a nasopharyngeal cancer (NPC) intensity-modulated radiation therapy (IMRT) plan. A total of 106 nine-field IMRT NPC plans were used in this study. Twenty-six plans were randomly selected as testing cases, and the remaining plans were used as the training data. A method employing geometric and dosimetric information was developed for OAR DVH prediction. The dosimetric information was derived from an initial dose calculation using a simple unoptimized conformal plan. The DVHs were also predicted using only the geometric information. The DVH prediction model was a generalized regression neural network (GRNN). Mean absolute error (MAE) and R ² values were introduced to evaluate DVH prediction accuracy. Significant differences in the DVH prediction accuracy were found between the method employing the geometric and dosimetric information and the method utilizing the geometric information for the brainstem ( R ² , 0.98 versus 0.95, p = 0.007; MAE, 3.52% versus 7.19%, p = 0.002), spinal cord ( R ² , 0.98 versus 0.96, p < 0.001; MAE, 2.80% versus 4.36%, p < 0.001), left optic nerve ( R ² , 0.90 versus 0.77, p = 0.014; MAE, 3.07% versus 11.29%, p = 0.025) and other organs. On average, the R ² value increased by ~6.7% and the MAE value decreased by ~46.7% after adding the dosimetric information to the DVH prediction. We developed a method for predicting DVHs of OARs in NPC IMRT plans by using geometric and dosimetric information. Adding dosimetric information can help predict the DVHs of OARs in NPC IMRT plans.

A quantitative method based on the electronic portal imaging system (EPID) and film was developed for MLC position and speed testing; this method was used for three MLC types (Millennium, MLCi, and Agility MLC). To determine the leaf position, a picket fence designed by the dynamic (DMLC) model was used. The full‐width half‐maximum (FWHM) values of each gap measured by EPID and EBT3 were converted to the gap width using the FWHM versus nominal gap width relationship. The algorithm developed for the picket fence analysis was able to quantify the gap width, the distance between gaps, and each individual leaf position. To determine the leaf speed, a 0.5 × 20 cm² MLC‐defined sliding gap was applied across a 14 × 20 cm² symmetry field. The linacs ran at a fixed‐dose rate. The use of different monitor units (MUs) for this test led to different leaf speeds. The effect of leaf transmission was considered in a speed accuracy analysis. The difference between the EPID and film results for the MLC position is less than 0.1 mm. For the three MLC types, twice the standard deviation (2 SD) is provided; 0.2, 0.4, and 0.4 mm for gap widths of three MLC types, and 0.1, 0.2, and 0.2 mm for distances between gaps. The individual leaf positions deviate from the preset positions within 0.1 mm. The variations in the speed profiles for the EPID and EBT3 results are consistent, but the EPID results are slightly better than the film results. Different speeds were measured for each MLC type. For all three MLC types, speed errors increase with increasing speed. The analysis speeds deviate from the preset speeds within approximately 0.01 cm s⁻¹. This quantitative analysis of MLC position and speed provides an intuitive evaluation for MLC quality assurance (QA).

To use a graphic processing unit (GPU) calculation engine to implement a fast 3D pre-treatment dosimetric verification procedure based on an electronic portal imaging device (EPID). The GPU algorithm includes the deconvolution and convolution method for the fluence-map calculations, the collapsed-cone convolution/superposition (CCCS) algorithm for the 3D dose calculations and the 3D gamma evaluation calculations. The results of the GPU-based CCCS algorithm were compared to those of Monte Carlo simulations. The planned and EPID-based reconstructed dose distributions in overridden-to-water phantoms and the original patients were compared for 6 MV and 10 MV photon beams in intensity-modulated radiation therapy (IMRT) treatment plans based on dose differences and gamma analysis. The total single-field dose computation time was less than 8 s, and the gamma evaluation for a 0.1-cm grid resolution was completed in approximately 1 s. The results of the GPU-based CCCS algorithm exhibited good agreement with those of the Monte Carlo simulations. The gamma analysis indicated good agreement between the planned and reconstructed dose distributions for the treatment plans. For the target volume, the differences in the mean dose were less than 1.8%, and the differences in the maximum dose were less than 2.5%. For the critical organs, minor differences were observed between the reconstructed and planned doses. The GPU calculation engine was used to boost the speed of 3D dose and gamma evaluation calculations, thus offering the possibility of true real-time 3D dosimetric verification.

The aim of this study was to combine a set of methods for use of radiochromic film dosimetry, including calibration, correction for lateral effects and a proposed triple-channel analysis. These methods can be applied to GAFCHROMIC EBT3 film dosimetry for radiation field analysis and verification of IMRT plans. A single-film exposure was used to achieve dose calibration, and the accuracy was verified based on comparisons with the square-field calibration method. Before performing the dose analysis, the lateral effects on pixel values were corrected. The position dependence of the lateral effect was fitted by a parabolic function, and the curvature factors of different dose levels were obtained using a quadratic formula. After lateral effect correction, a triple-channel analysis was used to reduce disturbances and convert scanned images from films into dose maps. The dose profiles of open fields were measured using EBT3 films and compared with the data obtained using an ionization chamber. Eighteen IMRT plans with different field sizes were measured and verified with EBT3 films, applying our methods, and compared to TPS dose maps, to check correct implementation of film dosimetry proposed here. The uncertainty of lateral effects can be reduced to ±1 cGy. Compared with the results of Micke A et al., the residual disturbances of the proposed triple-channel method at 48, 176 and 415 cGy are 5.3%, 20.9% and 31.4% smaller, respectively. Compared with the ionization chamber results, the difference in the off-axis ratio and percentage depth dose are within 1% and 2%, respectively. For the application of IMRT verification, there were no difference between two triple-channel methods. Compared with only corrected by triple-channel method, the IMRT results of the combined method (include lateral effect correction and our present triple-channel method) show a 2% improvement for large IMRT fields with the criteria 3%/3 mm.