ArticleJournal of applied clinical medical physics2023
A convolutional neural network model for EPID-based non-transit dosimetry.
Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 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
- Application of a model-based water-equivalent EPID image conversion algorithm for linac beam QA.Journal of applied clinical medical physics · 2025Article
- Detection of the failed-tolerance causes of electronic-portal-imaging-device-basedPhysics and imaging in radiation oncology · 2025Article
- A convolutional neural network model for EPID-based non-transit dosimetry.Journal of applied clinical medical physics · 2023Article
- Dose Characteristics of a Deep Learning Model for EPID-basedJournal of medical physicsArticle
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6 authors.
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Abstract
purposeTo develop an alternative computational approach for EPID-based non-transit dosimetry using a convolutional neural network model.
methodA U-net followed by a non-trainable layer named True Dose Modulation recovering the spatialized information was developed. The model was trained on 186 Intensity-Modulated Radiation Therapy Step & Shot beams from 36 treatment plans of different tumor locations to convert grayscale portal images into planar absolute dose distributions. Input data were acquired from an amorphous-Silicon Electronic Portal Image Device and a 6 MV X-ray beam. Ground truths were computed from a conventional kernel-based dose algorithm. The model was trained by a two-step learning process and validated through a five-fold cross-validation procedure with sets of training and validation of 80% and 20%, respectively. A study regarding the dependance of the amount of training data was conducted. The performance of the model was evaluated from a quantitative analysis based the ϒ-index, absolute and relative errors computed between the inferred dose distributions and ground truths for six square and 29 clinical beams from seven treatment plans. These results were also compared to those of an existing portal image-to-dose conversion algorithm.
resultsFor the clinical beams, averages of ϒ-index and ϒ-passing rate (2%-2mm > 10% D
conclusionA deep learning-based model was developed to convert portal images into absolute dose distributions. The accuracy obtained shows that this method has great potential for EPID-based non-transit dosimetry.
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