Development of EPID-based dosimetry for FFF-beam verification in radiation therapy

Thongsawad, Sangutid; Chanton, T.; Saiyo, Nipon; Udee, Nuntawat

doi:10.1088/1742-6596/1285/1/012031

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Our EPID-based dosimetry model has been published elsewhere [30] with less validated plans. Therefore, this study has the effort to implement the EPID-based dosimetry model for SRS and SRT plans, and the results demonstrated a good agreement between EPID-based dosimetry and traditional detector arrays measurement (MapCHECK).…”

Section: Discussionmentioning

confidence: 99%

Planar EPID-Based Dosimetry for SRS and SRT Patient-Specific QA

Thongsawad

Chanton

Saiyo

et al. 2021

Life

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The study’s purpose was to develop and validate Electronic Portal Imaging Device (EPID)-based dosimetry for Stereotactic Radiosurgery (SRS) and Stereotactic Radiation Therapy (SRT) patient-specific Quality Assurance (QA). The co-operation between extended Source-to-Imager Distance (SID) to reduce the saturation effect and simplify the EPID-based dosimetry model was used to perform patient-specific QA in SRS and SRT plans. The four parameters were included for converting the image to dose at depth 10 cm; dose-response linearity with MU, beam profile correction, collimator scatter and water kernel. The model accuracy was validated with 10 SRS/SRT plans. The traditional diode arrays with MapCHECK were also used to perform patient-specific QA for assuring model accuracy. The 150 cm-SID was found a possibility to reduce the saturation effect. The result of model accuracy was found good agreement between our EPID-based dosimetry and TPS calculation with GPR more than 98% for gamma criteria of 3%/3 mm, more than 95% for gamma criteria of 2%/2 mm, and the results related to the measurement with MapCHECK. This study demonstrated the method to perform SRT and SRT patient-specific QA using EPID-based dosimetry in the FFF beam by co-operating between the extended SID that can reduce the saturation effect and estimate the planar dose distribution with the in-house model.

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Section: Discussionmentioning

confidence: 99%

Planar EPID-Based Dosimetry for SRS and SRT Patient-Specific QA

Thongsawad

Chanton

Saiyo

et al. 2021

Life

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“…To ensure the consistency of the MLC and beam angle, an EPID detector is permanently attached inside the Halcyon. EPID images can be extracted to provide dose distribution information; as a dosimetry method for verification before and during therapy [5][6][7][8][9][10][11][12][13][14]. One type of dosimetry by EPID is non-transit dosimetry, which can predict the dose inside the patient for pre-treatment verification by measuring doses at a certain height without an attenuator.…”

Section: Introductionmentioning

confidence: 99%

Comparison of deep learning models for building two-dimensional non-transit EPID Dosimetry on Varian Halcyon

Ramadhan,

Wibowo,

Prajitno

et al. 2024

Rep Pract Oncol Radiother.

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Background This study compared the effectiveness of five deep learning models in constructing non-transit dosimetry with an a-Si electronic portal imaging device (EPID) on Varian Halcyon. Deep learning model is increasingly used to support prediction and decision-making in several fields including oncology and radiotherapy. Materials and methods Forty-seven unique plans of data obtained from breast cancer patients were calculated using Eclipse treatment planning system (TPS) and extracted from DICOM format as the ground truth. Varian Halcyon was then used to irradiate the a-Si 1200 EPID detector without an attenuator. The EPID and TPS images were augmented and divided randomly into two groups of equal sizes to distinguish the validation and training–test data. Five different deep learning models were then created and validated using a gamma index of 3%/3 mm. Results Four models successfully improved the similarity of the EPID images and the TPS-generated planned dose images. Meanwhile, the mismatch of the constituent components and number of parameters could cause the models to produce wrong results. The average gamma pass rates were 90.07 ± 4.96% for A-model, 77.42 ± 7.18% for B-model, 79.60 ± 6.56% for C-model, 80.21 ± 5.88% for D-model, and 80.47 ± 5.98% for E-model. Conclusion The deep learning model is proven to run fast and can increase the similarity of EPID images with TPS images to build non-transit dosimetry. However, more cases are needed to validate this model before being used in clinical activities.

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Development of EPID-based dosimetry for FFF-beam verification in radiation therapy

Cited by 2 publications

References 25 publications

Planar EPID-Based Dosimetry for SRS and SRT Patient-Specific QA

Planar EPID-Based Dosimetry for SRS and SRT Patient-Specific QA

Comparison of deep learning models for building two-dimensional non-transit EPID Dosimetry on Varian Halcyon

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