ArticleBMC cancer2023
Using RegGAN to generate synthetic CT images from CBCT images acquired with different linear accelerators.
Article in BMC cancer, 2023. 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.
- ART workflow based on CBCT and end-to-end clinical dynamic phantom validation.BMC medical imaging · 2026Article
- Article
- Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration.European journal of nuclear medicine and molecular imaging · 2025Article
- CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory.Scientific reports · 2025Article
- A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer.Physics and imaging in radiation oncology · 2025Review
- Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies.IEEE open journal of engineering in medicine and biology · 2025Article
- Assessing intra- and interfraction motion and its dosimetric impacts on cervical cancer adaptive radiotherapy based on 1.5T MR-Linac.Radiation oncology (London, England) · 2024Article
- Dual-encoder architecture for metal artifact reduction for kV-cone-beam CT images in head and neck cancer radiotherapy.Scientific reports · 2024Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundThe goal was to investigate the feasibility of the registration generative adversarial network (RegGAN) model in image conversion for performing adaptive radiation therapy on the head and neck and its stability under different cone beam computed tomography (CBCT) models.
methodsA total of 100 CBCT and CT images of patients diagnosed with head and neck tumors were utilized for the training phase, whereas the testing phase involved 40 distinct patients obtained from four different linear accelerators. The RegGAN model was trained and tested to evaluate its performance. The generated synthetic CT (sCT) image quality was compared to that of planning CT (pCT) images by employing metrics such as the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). Moreover, the radiation therapy plan was uniformly applied to both the sCT and pCT images to analyze the planning target volume (PTV) dose statistics and calculate the dose difference rate, reinforcing the model's accuracy.
resultsThe generated sCT images had good image quality, and no significant differences were observed among the different CBCT modes. The conversion effect achieved for Synergy was the best, and the MAE decreased from 231.3 ± 55.48 to 45.63 ± 10.78; the PSNR increased from 19.40 ± 1.46 to 26.75 ± 1.32; the SSIM increased from 0.82 ± 0.02 to 0.85 ± 0.04. The quality improvement effect achieved for sCT image synthesis based on RegGAN was obvious, and no significant sCT synthesis differences were observed among different accelerators.
conclusionThe sCT images generated by the RegGAN model had high image quality, and the RegGAN model exhibited a strong generalization ability across different accelerators, enabling its outputs to be used as reference images for performing adaptive radiation therapy on the head and neck.
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