ArticleMedical physics2025
Evaluation of model uncertainty in AI-based synthetic CT generation from CBCT for abdominal adaptive radiotherapy.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Article
- AI-assisted preoperative surgical planning for dental implant.Journal of translational medicine · 2025Article
- 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
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Abstract
backgroundThe synthesis of CT from CBCT images using AI methods has been explored in radiotherapy to improve adaptive workflows. However, the model training process can be particularly challenging for the abdominal region due to dataset disparities between CT and CBCT images caused by organ motion, low soft tissue contrast, and inconsistencies in air volumes. These factors might impact the implicit prediction uncertainties, which are not actively considered on the synthetized images, overlooking poorly predicted image regions that might lead to inaccuracies in the dose calculation. PURPOSE: To evaluate the impact of the model uncertainty on the predicted Hounsfield Units (HU) and dose calculation on synthetic CT (sCT) for abdominal patients.
methodsCBCT images from 65 abdominal patients were retrospectively used to generate sCT images. Rigid image registration (RIR) and deformable image registration (DIR) were individually implemented to create two datasets (D1 and D2) to train (80%), validate (10%), and test (10%) three models (M1: Unet, M2: Bayes-Unet, M3: cycle-GAN). Treatment plans were made on the ground truth CT (
resultsAcross the three models {M1, M2, M3} trained with D1 and D2, the MAE were {50.9 ± 13.3} and {40.9 ± 11.5}, respectively, the RMSE were {68.3 ± 13.5} and {62.2 ± 10.7}, respectively, and the SSIM were {0.89 ± 0.05} and {0.94 ± 0.05}, respectively. For D1 and D2, the gamma rates were {96.3 ± 1.04} and {97.3 ± 0.2}, respectively. No major differences in DVH were noticed between
conclusionUsing DIR resulted in improved performance across all three models. Metrics used to evaluate synthetic image accuracy might not reflect the uncertainty implications in image quality and dose calculations, which suggests the benefit of displaying uncertainty errors in AI generated sCT as a potential strategy to improve the evaluation of intra-fraction changes used for adaptive abdominal radiotherapy.
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