{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T21:09:07Z","timestamp":1773868147257,"version":"3.50.1"},"reference-count":20,"publisher":"IOP Publishing","issue":"5","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> 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). <jats:italic>Approach.<\/jats:italic> 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\u2019s innovations handle varying resolutions, and is efficient long-range dependency capture. <jats:italic>Main Results.<\/jats:italic> 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. <jats:italic>Significance.<\/jats:italic> 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\u2019s ability to preserve both global and local anatomical features shows potential for improving tumor targeting, adaptive radiotherapy planning, and clinical decision-making.<\/jats:p>","DOI":"10.1088\/2057-1976\/adf4ee","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T22:47:46Z","timestamp":1753742866000},"page":"055013","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing synthetic pelvic CT generation from CBCT using vision transformer with adaptive fourier neural operators"],"prefix":"10.1088","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4315-0178","authenticated-orcid":true,"given":"Rashmi","family":"Bhaskara","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8120-1821","authenticated-orcid":true,"given":"Oluwaseyi","family":"Oderinde","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"bpexadf4eebib1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2018.8006","article-title":"Long-term outcomes of stereotactic body radiotherapy for low-risk and intermediate-risk prostate cancer","volume":"2","author":"Kishan","year":"2019","journal-title":"JAMA Netw Open"},{"key":"bpexadf4eebib2","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.radonc.2013.08.030","article-title":"Stereotactic body radiotherapy for localized prostate cancer: pooled analysis from a multi-institutional consortium of prospective phase II trials","volume":"109","author":"King","year":"2013","journal-title":"Radiother. Oncol."},{"key":"bpexadf4eebib3","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1056\/NEJMoa2403365","article-title":"Phase 3 trial of stereotactic body radiotherapy in localized prostate cancer","volume":"391","author":"van As","year":"2024","journal-title":"N Engl J Med."},{"key":"bpexadf4eebib4","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.1002\/mp.12190","article-title":"Fast shading correction for cone beam CT in radiation therapy via sparse sampling on planning CT","volume":"44","author":"Shi","year":"2017","journal-title":"Med. Phys."},{"key":"bpexadf4eebib5","doi-asserted-by":"publisher","first-page":"5719","DOI":"10.1088\/0031-9155\/53\/20\/010","article-title":"Shading correction algorithm for improvement of cone-beam CT images in radiotherapy","volume":"53","author":"Marchant","year":"2008","journal-title":"Physics in Medicine & Biology"},{"key":"bpexadf4eebib6","doi-asserted-by":"publisher","first-page":"5395","DOI":"10.1118\/1.3483260","article-title":"Shading correction for on-board cone-beam CT in radiation therapy using planning MDCT images","volume":"37","author":"Niu","year":"2010","journal-title":"Med. Phys."},{"key":"bpexadf4eebib7","doi-asserted-by":"publisher","first-page":"125002","DOI":"10.1088\/1361-6560\/ab22f9","article-title":"Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy","volume":"64","author":"Liang","year":"2019","journal-title":"Physics in Medicine & Biology"},{"key":"bpexadf4eebib8","doi-asserted-by":"publisher","first-page":"5317","DOI":"10.1002\/mp.15684","article-title":"Synthetic CT generation based on CBCT using respath-cycleGAN","volume":"49","author":"Deng","year":"2022","journal-title":"Med. Phys."},{"key":"bpexadf4eebib9","doi-asserted-by":"publisher","first-page":"3998","DOI":"10.1002\/mp.13656","article-title":"Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography","volume":"46","author":"Harms","year":"2019","journal-title":"Med. Phys."},{"key":"bpexadf4eebib10","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.2548","article-title":"Cone beam computed tomography image quality improvement using a deep convolutional neural network","volume":"10","author":"Kida","year":"2018","journal-title":"Cureus"},{"key":"bpexadf4eebib11","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1002\/mp.13978","article-title":"Synthetic CT generation from CBCT images via deep learning","volume":"47","author":"Chen","year":"2020","journal-title":"Med. Phys."},{"key":"bpexadf4eebib12","article-title":"MRI-based synthetic CT generation using deep convolutional neural network","volume":"vol 10949","author":"Lei","year":"2019"},{"key":"bpexadf4eebib13","doi-asserted-by":"publisher","first-page":"6624","DOI":"10.1038\/s41598-023-33472-w","article-title":"CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma","volume":"13","author":"Chen","year":"2023","journal-title":"Sci. Rep."},{"key":"bpexadf4eebib14","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-61492-7","article-title":"Synthetic CT generation based on CBCT using improved vision transformer CycleGAN","volume":"14","author":"Hu","year":"2024","journal-title":"Sci. Rep."},{"key":"bpexadf4eebib15","doi-asserted-by":"publisher","first-page":"1847","DOI":"10.1002\/mp.16704","article-title":"CBCT-based synthetic CT image generation using conditional denoising diffusion probabilistic model","volume":"51","author":"Peng","year":"2024","journal-title":"Med. Phys."},{"key":"bpexadf4eebib16","author":"Guibas","year":"2021"},{"key":"bpexadf4eebib17","first-page":"23","article-title":"A threshold selection method from gray-level histograms","volume":"11","author":"Otsu","year":"1975","journal-title":"Automatica"},{"key":"bpexadf4eebib18","doi-asserted-by":"publisher","first-page":"20190023","DOI":"10.1259\/bjro.20190023","article-title":"Radiation dosimetry changes in radiotherapy treatment plans for adult patients arising from the selection of the CT image reconstruction kernel","volume":"1","author":"Davis","year":"2019","journal-title":"BJR Open"},{"key":"bpexadf4eebib19","doi-asserted-by":"publisher","first-page":"969463","DOI":"10.3389\/fonc.2022.969463","article-title":"Synthetic CT generation for MRI-guided adaptive radiotherapy in prostate cancer","volume":"12","author":"Hsu","year":"2022","journal-title":"Frontiers in Oncology"},{"key":"bpexadf4eebib20","first-page":"tzae014","article-title":"Effect of synthetic CT on dose-derived toxicity predictors for MR-only prostate radiotherapy","volume":"6","author":"Thomas","year":"2024","journal-title":"BJR\u2014Open"}],"container-title":["Biomedical Physics &amp; Engineering Express"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/adf4ee","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/adf4ee\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/adf4ee","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/adf4ee\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/adf4ee\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/adf4ee\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/adf4ee\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/adf4ee\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T14:35:32Z","timestamp":1754922932000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/adf4ee"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,11]]},"references-count":20,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,8,11]]},"published-print":{"date-parts":[[2025,9,30]]}},"URL":"https:\/\/doi.org\/10.1088\/2057-1976\/adf4ee","relation":{},"ISSN":["2057-1976"],"issn-type":[{"value":"2057-1976","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,11]]},"assertion":[{"value":"Enhancing synthetic pelvic CT generation from CBCT using vision transformer with adaptive fourier neural operators","name":"article_title","label":"Article Title"},{"value":"Biomedical Physics & Engineering Express","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2025-06-02","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-07-28","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-08-11","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}