A novel methodology for the optimization of transmission and dosimetric leaf gap parameters

DiCostanzo, D.J.; Ayan, A.S.

doi:10.1002/acm2.13565

Cited by 3 publications

(3 citation statements)

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“…It implies that some institutions possibly had failed to adjust the MLC modeling parameters and dose calculation accuracy has been suboptimal. Recently, there have been several proposals by which specific test fields and/or VMAT fields were used to optimize the parameters based on gamma passing rates 6–8 . Xue et al.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, there have been several proposals by which specific test fields and/or VMAT fields were used to optimize the parameters based on gamma passing rates. [6][7][8] Xue et al provided a simple method to obtain the optimal DLG value using clinical VMAT plans and measured dose distribution with an ArcCHECK diode array (Sun Nuclear). 7 DiCostanzo et al used two standard intensity modulated radiation therapy (IMRT) commissioning plans and measured doses with a MapCHECK2 diode array (Sun Nuclear) to optimize the DLG and TF based on the mean gammaindex as the cost function during optimization.…”

Section: Introductionmentioning

confidence: 99%

“…7 DiCostanzo et al used two standard intensity modulated radiation therapy (IMRT) commissioning plans and measured doses with a MapCHECK2 diode array (Sun Nuclear) to optimize the DLG and TF based on the mean gammaindex as the cost function during optimization. 8 Those studies relied on the method of gamma analysis, which is the most accepted method of evaluating the degree of agreement between calculated and measured dose distributions in 2D and 3D. 9,10 However, it has been demonstrated that the comparison between measured and TPS-calculated dose distribution using gamma analysis fails to detect MLC modeling errors.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning‐based detection and classification of multi‐leaf collimator modeling errors in volumetric modulated radiation therapy

Nakamura,

Sakai,

Ishizaka

et al. 2023

J Applied Clin Med Phys

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PurposeThe purpose of this study was to create and evaluate deep learning‐based models to detect and classify errors of multi‐leaf collimator (MLC) modeling parameters in volumetric modulated radiation therapy (VMAT), namely the transmission factor (TF) and the dosimetric leaf gap (DLG).MethodsA total of 33 clinical VMAT plans for prostate and head‐and‐neck cancer were used, assuming a cylindrical and homogeneous phantom, and error plans were created by altering the original value of the TF and the DLG by ± 10, 20, and 30% in the treatment planning system (TPS). The Gaussian filters of and 1.0 were applied to the planar dose maps of the error‐free plan to mimic the measurement dose map, and thus dose difference maps between the error‐free and error plans were obtained. We evaluated 3 deep learning‐based models, created to perform the following detections/classifications: (1) error‐free versus TF error, (2) error‐free versus DLG error, and (3) TF versus DLG error. Models to classify the sign of the errors were also created and evaluated. A gamma analysis was performed for comparison.ResultsThe detection and classification of TF and DLG error were feasible for ; however, a considerable reduction of accuracy was observed for depending on the magnitude of error and treatment site. The sign of errors was detectable by the specifically trained models for and 1.0. The gamma analysis could not detect errors.ConclusionsWe demonstrated that the deep learning‐based models could feasibly detect and classify TF and DLG errors in VMAT dose distributions, depending on the magnitude of the error, treatment site, and the degree of mimicked measurement doses.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning‐based detection and classification of multi‐leaf collimator modeling errors in volumetric modulated radiation therapy

Nakamura,

Sakai,

Ishizaka

et al. 2023

J Applied Clin Med Phys

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A novel methodology for the optimization of transmission and dosimetric leaf gap parameters

DiCostanzo

Ayan

2022

J Applied Clin Med Phys

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Optimization of dosimetric leaf gap (DLG) and transmission is commonly performed through a manual trial and error process, which can lead to sub-optimal values. The purpose of this work is to create an alternative automated optimization process that provides the optimal DLG and transmission pair for use in a clinical setting. Methods: Utilizing the treatment planning system application programming interface, a phase space of clinically viable DLG and transmission pairs was generated. The phase space contained 51,051 dose planes for DLGs between 0.0 and 2.5 mm and transmission values between 0.01% and 2.5%. Thirteen plans were measured for multiple multileaf collimator types and nominal beam energies. The optimization minimized the mean γ-index and maximized the γindex pass rate. The optimized values were validated using five plans excluded from the optimization. Results: Of the nominal beam energies and multileaf collimator system (MLC)type combinations tested, 6/7 showed an increase in γ-index pass rate and a decrease in mean γ-index signifying better agreement between measurement and calculation. When comparing the optimized DLG and transmission values to the clinically implemented values identified via an iterative method, 5/7 energy, and MLC type combinations showed no statistically significant changes. In addition, the optimized values were benchmarked against three Task Group 119 plans with published γ-index pass rates, which had been held out of the optimization. For those plans, the optimized DLG and transmission values provided the same or better γ-index pass rates. Conclusion:We presented a novel and viable automated alternative to current approaches of selecting the DLG and transmission parameters. This method will reduce the time required to determine the clinically acceptable DLG and transmission parameters and ensure optimality for the plans included in the optimization.

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Design and performance of a novel multi‐leaf collimator composed of leaves with fixed and movable layers

Xu,

Niu,

Liu

et al. 2024

Medical Physics

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BackgroundThe multi‐leaf collimator (MLC) is an advanced device utilized for beam shaping and intensity modulation in radiotherapy. With the framework of the contemporary single‐layer MLC featuring a rounded leaf tip, the leaf tip transmission and leakage exert a considerable influence on radiotherapy.PurposeTo scale down the leakage and transmission from the leaf gap when the opposite leaves are closed.MethodsThis study proposes a new design of MLC, named dynamic leaf machine (DLM), specifically engineered to diminish the transmission and leakage from the MLC leaf tip. The DLM incorporates an innovative leaf configuration that involves a combination of fixed and movable layers in a single MLC leaf named fixed and movable (FM) layers leaf, which is advantageous in dealing with transmission and leakage from the leaf tip by employing a staggered arrangement of the fixed and movable layer when the opposite leaves are closed. Subsequently, the DLM is assembled on the Elekta VERSA HD accelerator to assess its performance, focusing on aspects of mechanical characters, transmission, leakage, and penumbra.ResultsIn comparison to conventional MLC leaves, the FM leaf design in the DLM has achieved a remarkable reduction in the leakage between opposed closed leaves, decreasing it from 48.41% to 0.41%. Additionally, the transmission between the adjacent leaves has been measured at 0.59%, and the penumbra in 16 mm × 32 mm field is 2.82 mm, aligning with the performance of conventional MLC.ConclusionsThe DLM with FM leaf performs a significant reduction in transmission from the opposite leaf tip in comparison to conventional MLC while maintaining minimal penumbra, effectively mitigating the transmission and leakage problem between opposing leaves, thereby enhancing the effect of radiotherapy.

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A novel methodology for the optimization of transmission and dosimetric leaf gap parameters

Cited by 3 publications

References 20 publications

Deep learning‐based detection and classification of multi‐leaf collimator modeling errors in volumetric modulated radiation therapy

Deep learning‐based detection and classification of multi‐leaf collimator modeling errors in volumetric modulated radiation therapy

A novel methodology for the optimization of transmission and dosimetric leaf gap parameters

Design and performance of a novel multi‐leaf collimator composed of leaves with fixed and movable layers

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