ArticleJournal of applied clinical medical physics2020
Dosimetric evaluation of synthetic CT generated with GANs for MRI-only proton therapy treatment planning of brain tumors.
Article in Journal of applied clinical medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- AI in Proton Therapy Treatment Planning: A Narrative Review.International journal of particle therapy · 2026Article
- Commissioning and clinical implementation of Monaco treatment planning system on a compact pencil beam scanning proton therapy system.Journal of applied clinical medical physics · 2026Article
- Principles of artificial intelligence in radiooncology.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Synthetic CT generation from CBCT using deep learning for adaptive radiotherapy in prostate cancer.Frontiers in radiology · 2025Article
- Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.Physical and engineering sciences in medicine · 2024Article
- Clinical implementation of a commercial synthetic computed tomography solution for radiotherapy treatment of glioblastoma.Physics and imaging in radiation oncology · 2024Article
- Review
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Using RegGAN to generate synthetic CT images from CBCT images acquired with different linear accelerators.BMC cancer · 2023Article
- Compensation cycle consistent generative adversarial networks (Comp-GAN) for synthetic CT generation from MR scans with truncated anatomy.Medical physics · 2023Article
- Article
- Evaluation of MRI-only based online adaptive radiotherapy of abdominal region on MR-linac.Journal of applied clinical medical physics · 2023Article
- Synthetic CT in Carbon Ion Radiotherapy of the Abdominal Site.Bioengineering (Basel, Switzerland) · 2023Article
- A Survey on Deep Learning for Precision Oncology.Diagnostics (Basel, Switzerland) · 2022Review
- The role of generative adversarial networks in brain MRI: a scoping review.Insights into imaging · 2022Article
- Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.Physics in medicine and biology · 2022Review
- Generative Adversarial Networks in Brain Imaging: A Narrative Review.Journal of imaging · 2022Review
- Please Place Your Seat in the Full Upright Position: A Technical Framework for Landing Upright Radiation Therapy in the 21Frontiers in oncology · 2022Article
- Facilitating MR-Guided Adaptive Proton Therapy in Children Using Deep Learning-Based Synthetic CT.International journal of particle therapy · 2022Article
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Authors and funding
10 authors.
Funding
Abstract
purposeThe purpose of this study was to address the dosimetric accuracy of synthetic computed tomography (sCT) images of patients with brain tumor generated using a modified generative adversarial network (GAN) method, for their use in magnetic resonance imaging (MRI)-only treatment planning for proton therapy.
methodsDose volume histogram (DVH) analysis was performed on CT and sCT images of patients with brain tumor for plans generated for intensity-modulated proton therapy (IMPT). All plans were robustly optimized using a commercially available treatment planning system (RayStation, from RaySearch Laboratories) and standard robust parameters reported in the literature. The IMPT plan was then used to compute the dose on CT and sCT images for dosimetric comparison, using RayStation analytical (pencil beam) dose algorithm. We used a second, independent Monte Carlo dose calculation engine to recompute the dose on both CT and sCT images to ensure a proper analysis of the dosimetric accuracy of the sCT images.
resultsThe results extracted from RayStation showed excellent agreement for most DVH metrics computed on the CT and sCT for the nominal case, with a mean absolute difference below 0.5% (0.3 Gy) of the prescription dose for the clinical target volume (CTV) and below 2% (1.2 Gy) for the organs at risk (OARs) considered. This demonstrates a high dosimetric accuracy for the generated sCT images, especially in the target volume. The metrics obtained from the Monte Carlo doses mostly agreed with the values extracted from RayStation for the nominal and worst-case scenarios (mean difference below 3%).
conclusionsThis work demonstrated the feasibility of using sCT generated with a GAN-based deep learning method for MRI-only treatment planning of patients with brain tumor in intensity-modulated proton therapy.
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