ArticleRadiation oncology (London, England)2022
A feasibility study for in vivo treatment verification of IMRT using Monte Carlo dose calculation and deep learning-based modelling of EPID detector response.
Article in Radiation oncology (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Improving patient treatment accuracy using transit dosimetry with Electronic Portal Imaging Device images and deep learning.Physics and imaging in radiation oncology · 2026Article
- CycleGan-based kV-to-MV image translation for potential in vivo dosimetry application: concept proposal and cross-institutional validation.BMC medical imaging · 2025Article
- Evaluation of a Monte Carlo based EPID system for patient-specific IMRT and VMAT quality assurance.Journal of applied clinical medical physics · 2025Article
- A Novel Approach for Position Verification and Dose Calculation through Local MVCT Reconstruction.Diagnostics (Basel, Switzerland) · 2024Article
- Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction.Journal of imaging informatics in medicine · 2024Article
- AAPM Task Group Report 307: Use of EPIDs for Patient-Specific IMRT and VMAT QA.Medical physics · 2023Review
- Artificial intelligence assists precision medicine in cancer treatment.Frontiers in oncology · 2022Review
- Dose Characteristics of a Deep Learning Model for EPID-basedJournal of medical physicsArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundThis paper describes the development of a predicted electronic portal imaging device (EPID) transmission image (TI) using Monte Carlo (MC) and deep learning (DL). The measured and predicted TI were compared for two-dimensional in vivo radiotherapy treatment verification.
methodsThe plan CT was pre-processed and combined with solid water and then imported into PRIMO. The MC method was used to calculate the dose distribution of the combined CT. The U-net neural network-based deep learning model was trained to predict EPID TI based on the dose distribution of solid water calculated by PRIMO. The predicted TI was compared with the measured TI for two-dimensional in vivo treatment verification.
resultsThe EPID TI of 1500 IMRT fields were acquired, among which 1200, 150, and 150 fields were used as the training set, the validation set, and the test set, respectively. A comparison of the predicted and measured TI was carried out using global gamma analyses of 3%/3 mm and 2%/2 mm (5% threshold) to validate the model's accuracy. The gamma pass rates were greater than 96.7% and 92.3%, and the mean gamma values were 0.21 and 0.32, respectively.
conclusionsOur method facilitates the modelling process more easily and increases the calculation accuracy when using the MC algorithm to simulate the EPID response, and has potential to be used for in vivo treatment verification in the clinic.
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