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Purpose: To investigate and improve the level of equivalency of Varian TrueBeam linear accelerators (linacs) in energy-, dosimetric leaf gap- (DLG) and jaw calibration., Methods: Eight linacs with four photon energies: 6 MV, 6 MV FFF, 10 MV FFF, and 15 MV, and three electron energies (on two linacs): 6, 9, and 12 MeV were commisioned and beam-matched. Initially, symmetry of lateral profiles was calibrated for maximum field size. Energy-matching was then performed for photons by adjusting diagonal profiles at maximum field size and depth of maximum dose to coincide with the reference linac, and for electrons by matching the range at percentage depth of ionization of 90%, 80%, and 50%. Calibration of DLG was performed for 6 MV and evaluated among the linacs. The relationship between DLG and the Gap value was investigated. A method using electronic portal imaging device (EPID) was developed and implemented for jaw calibration., Results: Symmetry calibration for photons (electrons) was within 1% (0.7%), further improving the vendor's acceptance criteria. Photon and electron energy-matching was within 0.5% and 0.1 mm, respectively. Calibration of DLG was within 0.032 mm among the linacs and utilizing the relationship between DLG and the Gap value resulted in an empirical calibration method which was implemented to simplify DLG adjustment. Using EPID-based method of calibration, evaluation of the jaw-positioning among the linacs for 30 cm × 30 cm field size was within 0.4 mm and in the junction area within 0.2 mm. Dose delivery error of VMAT-plans were at least 99.2% gamma pass rate (1%, 1 mm)., Conclusions: High level of equivalency, beyond clinically accepted criteria, of TrueBeam linacs could be achieved which reduced dose delivery systematic errors and increased confidence in interchanging patients among linacs., (© 2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Purpose: Multileaf collimator (MLC) tracking is being clinically pioneered to continuously compensate for thoracic and pelvic motion during radiotherapy. The purpose of this work was to characterize the performance of two MLC leaf-fitting algorithms, direct optimization and piecewise optimization, for real-time motion compensation with different plan complexity and tumor trajectories., Methods: To test the algorithms, both in silico and phantom experiments were performed. The phantom experiments were performed on a Trilogy Varian linac and a HexaMotion programmable motion platform. High and low modulation VMAT plans for lung and prostate cancer cases were used along with eight patient-measured organ-specific trajectories. For both MLC leaf-fitting algorithms, the plans were run with their corresponding patient trajectories. To compare algorithms, the average exposure errors, i.e., the difference in shape between ideal and fitted MLC leaves by the algorithm, plan complexity and system latency of each experiment were calculated., Results: Comparison of exposure errors for the in silico and phantom experiments showed minor differences between the two algorithms. The average exposure errors for in silico experiments with low/high plan complexity were 0.66/0.88 cm 2 for direct optimization and 0.66/0.88 cm 2 for piecewise optimization, respectively. The average exposure errors for the phantom experiments with low/high plan complexity were 0.73/1.02 cm 2 for direct and 0.73/1.02 cm 2 for piecewise optimization, respectively. The measured latency for the direct optimization was 226 ± 10 ms and for the piecewise algorithm was 228 ± 10 ms. In silico and phantom exposure errors quantified for each treatment plan demonstrated that the exposure errors from the high plan complexity (0.96 cm 2 mean, 2.88 cm 2 95% percentile) were all significantly different from the low plan complexity (0.70 cm 2 mean, 2.18 cm 2 95% percentile) (P < 0.001, two-tailed, Mann-Whitney statistical test)., Conclusions: The comparison between the two leaf-fitting algorithms demonstrated no significant differences in exposure errors, neither in silico nor with phantom experiments. This study revealed that plan complexity impacts the overall exposure errors significantly more than the difference between the algorithms., (© 2019 American Association of Physicists in Medicine.)

Purpose: Prostate motion during radiation therapy (ie, intrafraction motion) can cause unwanted loss of radiation dose to the prostate and increased dose to the surrounding organs at risk. A compact but general statistical description of this motion could be useful for simulation of radiation therapy delivery or margin calculations. We investigated whether prostate motion could be modeled with a random walk model., Methods and Materials: Prostate motion recorded during 548 radiation therapy fractions in 17 patients was analyzed and used for input in a random walk prostate motion model. The recorded motion was categorized on the basis of whether any transient excursions (ie, rapid prostate motion in the anterior and superior direction followed by a return) occurred in the trace and transient motion. This was separately modeled as a large step in the anterior/superior direction followed by a returning large step. Random walk simulations were conducted with and without added artificial transient motion using either motion data from all observed traces or only traces without transient excursions as model input, respectively., Results: A general estimate of motion was derived with reasonable agreement between simulated and observed traces, especially during the first 5 minutes of the excursion-free simulations. Simulated and observed diffusion coefficients agreed within 0.03, 0.2 and 0.3 mm 2 /min in the left/right, superior/inferior, and anterior/posterior directions, respectively. A rapid increase in variance at the start of observed traces was difficult to reproduce and seemed to represent the patient's need to adjust before treatment. This could be estimated somewhat using artificial transient motion., Conclusions: Random walk modeling is feasible and recreated the characteristics of the observed prostate motion. Introducing artificial transient motion did not improve the overall agreement, although the first 30 seconds of the traces were better reproduced. The model provides a simple estimate of prostate motion during delivery of radiation therapy.

Purpose: A study of real-time adaptive radiotherapy systems was performed to test the hypothesis that, across delivery systems and institutions, the dosimetric accuracy is improved with adaptive treatments over non-adaptive radiotherapy in the presence of patient-measured tumor motion., Methods and Materials: Ten institutions with robotic(2), gimbaled(2), MLC(4) or couch tracking(2) used common materials including CT and structure sets, motion traces and planning protocols to create a lung and a prostate plan. For each motion trace, the plan was delivered twice to a moving dosimeter; with and without real-time adaptation. Each measurement was compared to a static measurement and the percentage of failed points for γ-tests recorded., Results: For all lung traces all measurement sets show improved dose accuracy with a mean 2%/2mm γ-fail rate of 1.6% with adaptation and 15.2% without adaptation (p<0.001). For all prostate the mean 2%/2mm γ-fail rate was 1.4% with adaptation and 17.3% without adaptation (p<0.001). The difference between the four systems was small with an average 2%/2mm γ-fail rate of <3% for all systems with adaptation for lung and prostate., Conclusions: The investigated systems all accounted for realistic tumor motion accurately and performed to a similar high standard, with real-time adaptation significantly outperforming non-adaptive delivery methods., (Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.)