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Objective: To develop a novel ionization chamber array dosimetry system, study its dosimetry characteristics, and perform preliminary tests for plan dose verification. Methods: The dosimetry characteristics of this new array were tested, including short‐term and long‐term reproducibility, dose linearity, dose rate dependence, field size dependence, and angular dependence. The open field and MLC field plans were designed for dose testing. Randomly select 30 patient treatment plans (10 intensity‐modulated radiation therapy [IMRT] plans and 20 volumetric modulated arc therapy [VMAT] plans) that have undergone dose verification using Portal Dosimetry to perform verification measurement and evaluate dose verification test results. Results: The dosimetry characteristics of the arrays all performed well. The gamma passing rates (3%/2 mm) were more than 96% for the combined open field and MLC field plans. The average gamma pass rates were (99.54 ± 0.58)% and (96.70 ± 3.41)% for the 10 IMRT plans and (99.32 ± 0.89)% and (94.91 ± 6.01)% for the 20 VMAT plans at the 3%/2 mm and 2%/2 mm criteria, respectively, which is similar to the Portal Dosimetry's measurement results. Conclusions: This novel ionization chamber array demonstrates good dosimetry characteristics and is suitable for clinical IMRT and VMAT plan verifications. [ABSTRACT FROM AUTHOR]

To investigate the difference of the fluence map optimization (FMO) and Stochastic platform optimization (SPO) algorithm in a newly‐introduced treatment planning system (TPS). Methods: 34 cervical cancer patients with definitive radiation were retrospectively analyzed. Each patient has four plans: FMO with fixed jaw plans (FMO‐FJ) and no fixed jaw plans (FMO‐NFJ); SPO with fixed jaw plans (SPO‐FJ) and no fixed jaw plans (SPO‐NFJ). Dosimetric parameters, Modulation Complexity Score (MCS), Gamma Pass Rate (GPR) and delivery time were analyzed among the four plans. Results: For target coverage, SPO‐FJ plans are the best ones (P ≤ 0.00). FMO plans are better than SPO‐NFJ plans (P ≤ 0.00). For OARs sparing, SPO‐FJ plans are better than FMO plans for mostly OARs (P ≤ 0.04). Additionally, SPO‐FJ plans are better than SPO‐NFJ plans (P ≤ 0.02), except for rectum V45Gy. Compared to SPO‐NFJ plans, the FMO plans delivered less dose to bladder, rectum, colon V40Gy and pelvic bone V40Gy (P ≤ 0.04). Meanwhile, the SPO‐NFJ plans showed superiority in MU, delivery time, MCS and GPR in all plans. In terms of delivery time and MCS, the SPO‐FJ plans are better than FMO plans. FMO‐FJ plans are better than FMO‐NFJ plans in delivery efficiency. MCSs are strongly correlated with PCTV length, which are negatively with PCTV length (P ≤ 0.03). The delivery time and MUs of the four plans are strongly correlated (P ≤ 0.02). Comparing plans with fixed or no fixed jaw in two algorithms, no difference was found in FMO plans in target coverage and minor difference in Kidney_L Dmean, Mu and delivery time between PCTV width≤15.5 cm group and >15.5 cm group. For SPO plans, SPO‐FJ plans showed more superiority in target coverage and OARs sparing than the SPO‐NFJ plans in the two groups. Conclusions: SPO‐FJ plans showed superiority in target coverage and OARs sparing, as well as higher delivery efficiency in the four plans. [ABSTRACT FROM AUTHOR]

Purpose: The Monte Carlo (MC) algorithm has been widely accepted as the most accurate algorithm for dosimetric calculations under various conditions in radiotherapy. However, the calculation time remains an important obstacle hindering the routine use of MC in clinical settings. In this study, full MC three‐dimensional dose distributions were obtained with the inputs of the total energy release per unit mass (TERMA) distributions and the electron density (ED) distributions using a convolutional neural network (CNN). A new Dose‐mixup data augmentation routine and training strategy are proposed and applied in the training process. Attempts were made to reduce the calculation time while ensuring that the calculation accuracy is comparable to that of the MC. Methods: Datasets were generated via the MC with random rectangular field sizes, random iso‐centers, and random gantry angles for head and neck computed tomography (CT) images with Mohan 6‐MV spectrum photon beams. 1500 samples were generated for the training set, and 150 samples were generated for the validation set. The T‐MC Net model was obtained with the Dose‐mixup data augmentation routine. The new CTs were used to test the performance of the model in the rectangular fields and the intensity‐modulated radiation therapy (IMRT) fields. The mean ± 95% confidence interval of gamma pass rates were calculated. Results: For 150 rectangular field test samples, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 90.11% ± 0.65%, 97.65% ± 0.31%, and 99.16% ± 0.19%, respectively. For the 100 IMRT field test samples, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 96.48% ± 0.28%, 99.14% ± 0.10%, and 99.63% ± 0.06%, respectively. For the 7‐fields IMRT plan, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 97.06%, 99.10%, and 99.52%, respectively. For the 9‐fields IMRT plan, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 98.16%, 99.61%, and 99.89%, respectively. Conclusions: The feasibility of calculating dose distribution using a CNN with the TERMA three‐dimensional distribution and ED distribution was established. The dosimetric results were comparable to those of the full MC. The accuracy and speed of the proposed approach make it a potential solution for full MC in radiotherapy. This method may be used as an acceleration engine for the dose algorithm and shows great potential for cases where fast dose calculations are needed. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]