Enhancing synthetic pelvic CT generation from CBCT using vision transformer with adaptive fourier neural operators.
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| Titel: | Enhancing synthetic pelvic CT generation from CBCT using vision transformer with adaptive fourier neural operators. |
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| Autoren: | Bhaskara R; Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Laboratory, School of Health Sciences, Purdue University, West Lafayette, IN, 47907, United States of America., Oderinde O; Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Laboratory, School of Health Sciences, Purdue University, West Lafayette, IN, 47907, United States of America.; Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN 46202, United States of America. |
| Quelle: | Biomedical physics & engineering express [Biomed Phys Eng Express] 2025 Aug 11; Vol. 11 (5). Date of Electronic Publication: 2025 Aug 11. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: IOP Publishing Ltd Country of Publication: England NLM ID: 101675002 Publication Model: Electronic Cited Medium: Internet ISSN: 2057-1976 (Electronic) Linking ISSN: 20571976 NLM ISO Abbreviation: Biomed Phys Eng Express Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol : IOP Publishing Ltd., [2015]- |
| MeSH-Schlagworte: | Cone-Beam Computed Tomography*/methods , Prostatic Neoplasms*/diagnostic imaging , Prostatic Neoplasms*/radiotherapy , Fourier Analysis* , Pelvis*/diagnostic imaging , Neural Networks, Computer* , Image Processing, Computer-Assisted*/methods, Humans ; Male ; Algorithms ; Radiosurgery ; Radiotherapy Planning, Computer-Assisted/methods |
| Abstract: | Objective. This study introduces a novel approach to improve Cone Beam CT (CBCT) image quality by developing a synthetic CT (sCT) generation method using CycleGAN with a Vision Transformer (ViT) and an Adaptive Fourier Neural Operator (AFNO). Approach. A dataset of 20 prostate cancer patients who received stereotactic body radiation therapy (SBRT) was used, consisting of paired CBCT and planning CT (pCT) images. The dataset was preprocessed by registering pCTs to CBCTs using deformation registration techniques, such as B-spline, followed by resampling to uniform voxel sizes and normalization. The model architecture integrates a CycleGAN with bidirectional generators, where the UNet generator is enhanced with a ViT at the bottleneck. AFNO functions as the attention mechanism for the ViT, operating on the input data in the Fourier domain. AFNO's innovations handle varying resolutions, and is efficient long-range dependency capture. Main Results. Our model improved significantly in preserving anatomical details and capturing complex image dependencies. The AFNO mechanism processed global image information effectively, adapting to interpatient variations for accurate sCT generation. Evaluation metrics like Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Cross Correlation (NCC), demonstrated the superiority of our method. Specifically, the model achieved an MAE of 9.71, PSNR of 37.08 dB, SSIM of 0.97, and NCC of 0.99, confirming its efficacy. Significance. The integration of AFNO within the CycleGAN UNet framework addresses Cone Beam CT image quality limitations. The model generates synthetic CTs that allow adaptive treatment planning during SBRT, enabling adjustments to the dose based on tumor response, thus reducing radiotoxicity from increased doses. This method's ability to preserve both global and local anatomical features shows potential for improving tumor targeting, adaptive radiotherapy planning, and clinical decision-making. (Creative Commons Attribution license.) |
| Contributed Indexing: | Keywords: CycleGAN; U-Net; adaptive radiotherapy; cone beam CT; prostate cancer; synthetic CT; vision transformer |
| Entry Date(s): | Date Created: 20250728 Date Completed: 20250811 Latest Revision: 20250811 |
| Update Code: | 20250811 |
| DOI: | 10.1088/2057-1976/adf4ee |
| PMID: | 40720966 |
| Datenbank: | MEDLINE |
Nájsť tento článok vo Web of Science | Abstract: | Objective. This study introduces a novel approach to improve Cone Beam CT (CBCT) image quality by developing a synthetic CT (sCT) generation method using CycleGAN with a Vision Transformer (ViT) and an Adaptive Fourier Neural Operator (AFNO). Approach. A dataset of 20 prostate cancer patients who received stereotactic body radiation therapy (SBRT) was used, consisting of paired CBCT and planning CT (pCT) images. The dataset was preprocessed by registering pCTs to CBCTs using deformation registration techniques, such as B-spline, followed by resampling to uniform voxel sizes and normalization. The model architecture integrates a CycleGAN with bidirectional generators, where the UNet generator is enhanced with a ViT at the bottleneck. AFNO functions as the attention mechanism for the ViT, operating on the input data in the Fourier domain. AFNO's innovations handle varying resolutions, and is efficient long-range dependency capture. Main Results. Our model improved significantly in preserving anatomical details and capturing complex image dependencies. The AFNO mechanism processed global image information effectively, adapting to interpatient variations for accurate sCT generation. Evaluation metrics like Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Cross Correlation (NCC), demonstrated the superiority of our method. Specifically, the model achieved an MAE of 9.71, PSNR of 37.08 dB, SSIM of 0.97, and NCC of 0.99, confirming its efficacy. Significance. The integration of AFNO within the CycleGAN UNet framework addresses Cone Beam CT image quality limitations. The model generates synthetic CTs that allow adaptive treatment planning during SBRT, enabling adjustments to the dose based on tumor response, thus reducing radiotoxicity from increased doses. This method's ability to preserve both global and local anatomical features shows potential for improving tumor targeting, adaptive radiotherapy planning, and clinical decision-making.<br /> (Creative Commons Attribution license.) |
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| ISSN: | 2057-1976 |
| DOI: | 10.1088/2057-1976/adf4ee |