ArticleRadiation oncology (London, England)2025
Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.
Article in Radiation oncology (London, England), 2025. 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.
- Efficient and rapid generation of high-quality CT images from CBCT using a flow matching model.Medical physics · 2026Article
- Quantitative magnetic resonance imaging for accurate delineation of radiotherapy: a narrative review.Translational cancer research · 2026Review
- Neural Autoregressive Modeling of Brain Aging.Deep generative models : 5th MICCAI workshop, DGM4MICCAI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings. DGM4MICCAI (Workshop) (5th : 2025 : Taejon-si, Korea) · 2026Article
- Self-learning GAN based synthetic CT generation: unlocking CBCT-based adaptive radiotherapy.Frontiers in oncology · 2026Article
- Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025Review
- Clinical implementation of deep learning-based synthetic CT for MRI-only volumetric modulated arc therapy in head and neck and pelvic cancer patients.Radiation oncology (London, England) · 2025Article
- The promise of artificial intelligence-assisted radiotherapy for prostate cancer in Morocco: a transformational opportunity.Frontiers in medicine · 2025Article
- Synthetic CT generation from CBCT using deep learning for adaptive radiotherapy in prostate cancer.Frontiers in radiology · 2025Article
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7 authors.
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Abstract
RATIONALE AND
objectivesThis study evaluated StarGAN, a deep learning model designed to generate synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) data using a single model. The goal was to provide accurate Hounsfield unit (HU) data for dose calculation to enable MRI simulation and adaptive radiation therapy (ART) using CBCT or MRI. We also compared the performance and benefits of StarGAN to the commonly used CycleGAN. MATERIALS AND
methodsStarGAN and CycleGAN were employed in this study. The dataset comprised 53 cases of pelvic cancer. Evaluation involved qualitative and quantitative analyses, focusing on synthetic image quality and dose distribution calculation.
resultsFor sCT generated from CBCT, StarGAN demonstrated superior anatomical preservation based on qualitative evaluation. Quantitatively, CycleGAN exhibited a lower mean absolute error (MAE) for the body (42.8 ± 4.3 HU) and bone (138.2 ± 20.3), whereas StarGAN produced a higher MAE for the body (50.8 ± 5.2 HU) and bone (153.4 ± 27.7 HU). Dosimetric evaluation showed a mean dose difference (DD) within 2% for the planning target volume (PTV) and body, with a gamma passing rate (GPR) > 90% under the 2%/2 mm criteria. For sCT generated from MRI, qualitative evaluation also favored the anatomical preservation provided by StarGAN. CycleGAN recorded a lower MAE (79.8 ± 14 HU for the body and 253.6 ± 30.9 HU for bone) compared with StarGAN (94.7 ± 7.4 HU for the body and 353.6 ± 34.9 HU for bone). Both models achieved a mean DD within 2% in the PTV and body, and GPR > 90%.
conclusionWhile CycleGAN exhibited superior quantitative metrics, StarGAN was better in anatomical preservation, highlighting its potential for sCT generation in radiotherapy.
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