Determination of dosimetric leaf gap using amorphous silicon electronic portal imaging device and its influence on intensity modulated radiotherapy dose delivery

Balasingh, S Timothy Peace; Singh, Inderjeet; Rafic, K Mohamathu; Babu, Sunil G.; Ravindran, B Paul

doi:10.4103/0971-6203.165072

Cited by 9 publications

(15 citation statements)

References 27 publications

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“…For example, measurement of DLG, a parameter found to be critical in the accuracy of IMRT, 2,4,19 has been found to vary based on the size of ion chamber used to measure it. 20 This underscores the necessity for physicists to critically analyze the measurement and validation process to ensure that, ultimately, the value used in their model is most adequate for their dose calculations.…”

Section: Discussionmentioning

confidence: 99%

Reference dataset of users’ photon beam modeling parameters for the Eclipse, Pinnacle, and RayStation treatment planning systems

et al. 2019

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Purpose:The aim of this work was to provide a novel description of how the radiotherapy community configures treatment planning system (TPS) radiation beam models for clinically used treatment machines. Here we describe the results of a survey of self-reported TPS beam modeling parameter values across different C-arm linear accelerators, beam energies, and multileaf collimator (MLC) configurations. Acquisition and Validation Methods:Beam modeling data were acquired via electronic survey implemented through the Imaging and Radiation Oncology Core (IROC) Houston Quality Assurance Center's online facility questionnaire. The survey was open to participation from January 2018 through January 2019 for all institutions monitored by IROC. After quality control, 2818 beam models were collected from 642 institutions. This survey, designed for Eclipse, Pinnacle, and RayStation, instructed physicists to report parameter values used to model the radiation source and MLC for each treatment machine and beam energy used clinically for intensity modulated radiation therapy. Parameters collected included the effective source/spot size, MLC transmission, dosimetric leaf gap, tongue and groove effect, and other non-dosimetric parameters specific to each TPS. To facilitate survey participation, instructions were provided on how to identify requested beam modeling parameters within each TPS environment.Data Format and Usage Notes: Numeric values of the beam modeling parameters are compiled and tabulated according to TPS and calculation algorithm, linear accelerator model class, beam energy, and MLC configuration. Values are also presented as distributions, ranging from the 2.5th to the 97.5th percentile.

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Section: Discussionmentioning

confidence: 99%

Reference dataset of users’ photon beam modeling parameters for the Eclipse, Pinnacle, and RayStation treatment planning systems

et al. 2019

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“…Varian MLC shaper software was used to create such a dynamic MLC sweeping gap field. MLC gap widths of 2 mm, 4 mm, 6 mm, 10 mm, 14 mm, 16 mm, and 20 mm were formed by both opposite leaves and uniformly extended over 10 × 10 cm 2 Yao et al and Timothy Peace Balasingh et al 8,9 The sweeping gap field moves from -60 mm to +60 mm at a constant speed with respect to MU to deliver dose. To define position of MLC, total number of 10 control points were created spaced one cm apart.…”

Section: Methodsmentioning

confidence: 99%

Validation of Dosimetric Leaf Gap (DLG) prior to its implementation in Treatment Planning System (TPS): TrueBeam™ millennium 120 leaf MLC

Shende¹,

Patel

2017

Reports of Practical Oncology & Radiotherapy

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“…For example, for a IMRT plan we examined in this study, the maximal change in dose profile was about 7% in the area of high dose gradient for the 25% change of the DLG, and the maximal change was about 7% in the area just outside of the field edge formed by MLCs for the 25% change of the TF. The variation in MLC modeling parameters can be attributed to differences in dose measurement tools and/or interinstitutional differences among the methods used to determine the parameters 3‐6 . The problem seems to be that it may be difficult to establish a consensus about the accuracy in determining the MLC modeling parameters in TPS commissioning in the community of radiation therapy 3 …”

Section: Introductionmentioning

confidence: 99%

“…We conducted the present study to investigate whether the radiomics‐based method combined with machine learning is effective in detecting errors in MLC modeling, that is, the MLC TF, and DLG errors and an MLC positional error distinguished from the error‐free condition, by evaluating fluence maps of clinical IMRT plans measured with an EPID. The spatial resolution of the EPID is superior to that of detector arrays, and the EPID shows high sensitivity in the tuning or quality control of MLC modeling parameters and in detecting MLC positional errors in patient‐specific IMRT QA 5,27‐32 . In this study, we used calculated and measured fluence difference maps instead of gamma maps, since it was shown that simple subtracted maps were more sensitive to MLC positional errors than gamma maps 26 …”

Section: Introductionmentioning

confidence: 99%

Detecting MLC modeling errors using radiomics‐based machine learning in patient‐specific QA with an EPID for intensity‐modulated radiation therapy

et al. 2021

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Purpose We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient‐specific quality assurance (QA) for intensity‐modulated radiation therapy (IMRT) with an electric portal imaging device (EPID). Methods Fluence maps measured with EPID for 38 beams from 19 clinical IMRT plans were assessed. Plans with various degrees of error in MLC modeling parameters [i.e., MLC transmission factor (TF) and dosimetric leaf gap (DLG)] and plans with an MLC positional error for comparison were created. For a total of 152 error plans for each type of error, we calculated fluence difference maps for each beam by subtracting the calculated maps from the measured maps. A total of 837 radiomic features were extracted from each fluence difference map, and we determined the number of features used for the training dataset in the machine learning models by using random forest regression. Machine learning models using the five typical algorithms [decision tree, k‐nearest neighbor (kNN), support vector machine (SVM), logistic regression, and random forest] for binary classification between the error‐free plan and the plan with the corresponding error for each type of error were developed. We used part of the total dataset to perform fourfold cross‐validation to tune the models, and we used the remaining test dataset to evaluate the performance of the developed models. A gamma analysis was also performed between the measured and calculated fluence maps with the criteria of 3%/2 and 2%/2 mm for all of the types of error. Results The radiomic features and its optimal number were similar for the models for the TF and the DLG error detection, which was different from the MLC positional error. The highest sensitivity was obtained as 0.913 for the TF error with SVM and logistic regression, 0.978 for the DLG error with kNN and SVM, and 1.000 for the MLC positional error with kNN, SVM, and random forest. The highest specificity was obtained as 1.000 for the TF error with a decision tree, SVM, and logistic regression, 1.000 for the DLG error with a decision tree, logistic regression, and random forest, and 0.909 for the MLC positional error with a decision tree and logistic regression. The gamma analysis showed the poorest performance in which sensitivities were 0.737 for the TF error and the DLG error and 0.882 for the MLC positional error for 3%/2 mm. The addition of another type of error to fluence maps significantly reduced the sensitivity for the TF and the DLG error, whereas no effect was observed for the MLC positional error detection. Conclusions Compared to the conventional gamma analysis, the radiomics‐based machine learning models showed higher sensitivity and specificity in detecting a single type of the MLC modeling error and the MLC positional error. Although the developed models need further improvement for detecting multiple types of error, radiomics‐based IMRT QA was shown to be a promising approach for detecting the MLC modeli...

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Determination of dosimetric leaf gap using amorphous silicon electronic portal imaging device and its influence on intensity modulated radiotherapy dose delivery

Cited by 9 publications

References 27 publications

Reference dataset of users’ photon beam modeling parameters for the Eclipse, Pinnacle, and RayStation treatment planning systems

Reference dataset of users’ photon beam modeling parameters for the Eclipse, Pinnacle, and RayStation treatment planning systems

Validation of Dosimetric Leaf Gap (DLG) prior to its implementation in Treatment Planning System (TPS): TrueBeam™ millennium 120 leaf MLC

Detecting MLC modeling errors using radiomics‐based machine learning in patient‐specific QA with an EPID for intensity‐modulated radiation therapy

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