ArticleJournal of applied clinical medical physics2022
Dosimetric assessment of patient dose calculation on a deep learning-based synthesized computed tomography image for adaptive radiotherapy.
Article in Journal of applied clinical medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO).La Radiologia medica · 2024Pooled it
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- Implementation of an AI-Driven Workflow for Daily Dose Reconstruction in Prostate Cancer Radiotherapy.Cancers · 2026Article
- Nasopharyngeal cancer adaptive radiotherapy with CBCT-derived synthetic CT: deep learning-based auto-segmentation precision and dose calculation consistency on a C-Arm linac.Radiation oncology (London, England) · 2025Article
- Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.Journal of applied clinical medical physics · 2025Article
- Article
- Dosimetric assessment of patient dose calculation on a deep learning-based synthesized computed tomography image for adaptive radiotherapy.Journal of applied clinical medical physics · 2022Article
- Accuracy and Feasibility of Synthetic CT for Lung Adaptive Radiotherapy: A Phantom Study.Technology in cancer research & treatmentArticle
- Evaluating a CBCT Correction Algorithm for Adaptive Radiotherapy.Technology in cancer research & treatmentArticle
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7 authors.
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Abstract
purposeDose computation using cone beam computed tomography (CBCT) images is inaccurate for the purpose of adaptive treatment planning. The main goal of this study is to assess the dosimetric accuracy of synthetic computed tomography (CT)-based calculation for adaptive planning in the upper abdominal region. We hypothesized that deep learning-based synthetically generated CT images will produce comparable results to a deformed CT (CTdef) in terms of dose calculation, while displaying a more accurate representation of the daily anatomy and therefore superior dosimetric accuracy.
methodsWe have implemented a cycle-consistent generative adversarial networks (CycleGANs) architecture to synthesize CT images from the daily acquired CBCT image with minimal error. CBCT and CT images from 17 liver stereotactic body radiation therapy (SBRT) patients were used to train, test, and validate the algorithm.
resultsThe synthetically generated images showed increased signal-to-noise ratio, contrast resolution, and reduced root mean square error, mean absolute error, noise, and artifact severity. Superior edge matching, sharpness, and preservation of anatomical structures from the CBCT images were observed for the synthetic images when compared to the CTdef registration method. Three verification plans (CBCT, CTdef, and synthetic) were created from the original treatment plan and dose volume histogram (DVH) statistics were calculated. The synthetic-based calculation shows comparatively similar results to the CTdef-based calculation with a maximum mean deviation of 1.5%.
conclusionsOur findings show that CycleGANs can produce reliable synthetic images for the adaptive delivery framework. Dose calculations can be performed on synthetic images with minimal error. Additionally, enhanced image quality should translate into better daily alignment, increasing treatment delivery accuracy.
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