CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory
Abstract Cone-beam computed tomography (CBCT) scans are widely used for real time monitoring and patient positioning corrections in image-guided radiation therapy (IGRT), enhancing the precision of radiation treatment. However, compared to high-quality computed tomography (CT) images, CBCT images su...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-92094-6 |
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| author | Can Hu Ning Cao Xiuhan Li Yang He Han Zhou |
| author_facet | Can Hu Ning Cao Xiuhan Li Yang He Han Zhou |
| author_sort | Can Hu |
| collection | DOAJ |
| description | Abstract Cone-beam computed tomography (CBCT) scans are widely used for real time monitoring and patient positioning corrections in image-guided radiation therapy (IGRT), enhancing the precision of radiation treatment. However, compared to high-quality computed tomography (CT) images, CBCT images suffer from severe artifacts and noise, which significantly hinder their application in IGRT. Therefore, synthesizing CBCT images into CT-like quality has become a critical necessity. In this study, we propose a hybrid U-Net diffusion model (HUDiff) based on Vision Transformer (ViT) and the information bottleneck theory to improve CBCT image quality. First, to address the limitations of the original U-Net in diffusion models, which primarily retain and transfer only local feature information, we introduce a ViT-based U-Net framework. By leveraging the self-attention mechanism, our model automatically focuses on different regions of the image during generation, aiming to better capture global features. Second, we incorporate a variational information bottleneck module at the base of the U-Net. This module filters out redundant and irrelevant information while compressing essential input data, thereby enabling more efficient summarization and better feature extraction. Finally, a dynamic modulation factor is introduced to balance the contributions of the main network and skip connections, optimizing the reverse denoising process in the diffusion model. We conducted extensive experiments on private Brain and Head & Neck datasets. The results, evaluated from multiple perspectives, demonstrate that our model outperforms state-of-the-art methods, validating its clinical applicability and robustness. In future clinical practice, our model has the potential to assist clinicians in formulating more precise radiation therapy plans. |
| format | Article |
| id | doaj-art-3db2385809ef4a1db9b8b16f5bf01983 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3db2385809ef4a1db9b8b16f5bf019832025-08-20T03:40:48ZengNature PortfolioScientific Reports2045-23222025-03-0115112110.1038/s41598-025-92094-6CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theoryCan Hu0Ning Cao1Xiuhan Li2Yang He3Han Zhou4School of Computer and Software, Hohai UniversitySchool of Computer and Software, Hohai UniversitySchool of Computer and Software, Hohai UniversitySchool of Computer and Software, Hohai UniversityDepartment of Radiation Oncology, The Fourth Affiliated Hospital of Nanjing Medical UniversityAbstract Cone-beam computed tomography (CBCT) scans are widely used for real time monitoring and patient positioning corrections in image-guided radiation therapy (IGRT), enhancing the precision of radiation treatment. However, compared to high-quality computed tomography (CT) images, CBCT images suffer from severe artifacts and noise, which significantly hinder their application in IGRT. Therefore, synthesizing CBCT images into CT-like quality has become a critical necessity. In this study, we propose a hybrid U-Net diffusion model (HUDiff) based on Vision Transformer (ViT) and the information bottleneck theory to improve CBCT image quality. First, to address the limitations of the original U-Net in diffusion models, which primarily retain and transfer only local feature information, we introduce a ViT-based U-Net framework. By leveraging the self-attention mechanism, our model automatically focuses on different regions of the image during generation, aiming to better capture global features. Second, we incorporate a variational information bottleneck module at the base of the U-Net. This module filters out redundant and irrelevant information while compressing essential input data, thereby enabling more efficient summarization and better feature extraction. Finally, a dynamic modulation factor is introduced to balance the contributions of the main network and skip connections, optimizing the reverse denoising process in the diffusion model. We conducted extensive experiments on private Brain and Head & Neck datasets. The results, evaluated from multiple perspectives, demonstrate that our model outperforms state-of-the-art methods, validating its clinical applicability and robustness. In future clinical practice, our model has the potential to assist clinicians in formulating more precise radiation therapy plans.https://doi.org/10.1038/s41598-025-92094-6 |
| spellingShingle | Can Hu Ning Cao Xiuhan Li Yang He Han Zhou CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory Scientific Reports |
| title | CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory |
| title_full | CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory |
| title_fullStr | CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory |
| title_full_unstemmed | CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory |
| title_short | CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory |
| title_sort | cbct to ct synthesis using a hybrid u net diffusion model based on transformers and information bottleneck theory |
| url | https://doi.org/10.1038/s41598-025-92094-6 |
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