ArticleRadiation oncology (London, England)2021
Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy.
Article in Radiation oncology (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 3 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
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- Deep learning-based prediction of interfractional anatomic variations in prostate cancer radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Article
- Knowledge-based treatment planning in breast cancer radiotherapy: comparing different machine learning algorithms.Physical and engineering sciences in medicine · 2026Article
- Foundation models in radiology: a primer for pediatric radiologists.Pediatric radiology · 2026Review
- Equivariant conditional diffusion model for head and neck CT image synthesis from CBCT.Medical physics · 2026Article
- Synthetic CT generation from cone-beam CT using deep learning for head-and-neck adaptive radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Data mining in pediatric radiology in the era of artificial intelligence.Pediatric radiology · 2026Review
- Low-dose CBCT image reconstruction: a review.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Review
- ART workflow based on CBCT and end-to-end clinical dynamic phantom validation.BMC medical imaging · 2026Article
- Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025Review
- [Advances in low-dose cone-beam computed tomography image reconstruction methods based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Interfractional body surface monitoring using daily cone-beam computed tomography imaging for pediatric adaptive proton therapy.Physics and imaging in radiation oncology · 2025Article
- Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.Radiation oncology (London, England) · 2025Article
- Pano-GAN: A Deep Generative Model for Panoramic Dental Radiographs.Journal of imaging · 2025Article
- Synthetic CT generation from CBCT using deep learning for adaptive radiotherapy in prostate cancer.Frontiers in radiology · 2025Article
- Adaptive treatment margins to reduce organs at risk dose in patients with no or minimal anatomical changes in radiotherapy of non-small cell lung cancer.Physics and imaging in radiation oncology · 2025Article
- Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy.Journal of imaging · 2024Article
- Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.Journal of applied clinical medical physics · 2024Article
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9 authors.
Funding
Abstract
objectiveTo develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy.
methodsThe CBCT/planning CT images of 170 patients undergoing thoracic radiotherapy were used for training and testing. The CBCT images were scanned under a fast protocol with 50% less clinical projection frames compared with standard chest M20 protocol. Training with aligned paired images was performed using conditional adversarial networks (so-called pix2pix), and training with unpaired images was carried out with cycle-consistent adversarial networks (cycleGAN) and AGGAN, through which sCT images were generated. The image quality and Hounsfield unit (HU) value of the sCT images generated by the three neural networks were compared. The treatment plan was designed on CT and copied to sCT images to calculated dose distribution.
resultsThe image quality of sCT images by all the three methods are significantly improved compared with original CBCT images. The AGGAN achieves the best image quality in the testing patients with the smallest mean absolute error (MAE, 43.5 ± 6.69), largest structural similarity (SSIM, 93.7 ± 3.88) and peak signal-to-noise ratio (PSNR, 29.5 ± 2.36). The sCT images generated by all the three methods showed superior dose calculation accuracy with higher gamma passing rates compared with original CBCT image. The AGGAN offered the highest gamma passing rates (91.4 ± 3.26) under the strictest criteria of 1 mm/1% compared with other methods. In the phantom study, the sCT images generated by AGGAN demonstrated the best image quality and the highest dose calculation accuracy.
conclusionsHigh-quality sCT images were generated from low-dose thoracic CBCT images by using the proposed AGGAN through unpaired CBCT and CT images. The dose distribution could be calculated accurately based on sCT images in radiotherapy.
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