{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T14:07:26Z","timestamp":1791295646249,"version":"4.1.0"},"reference-count":56,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["EB033994"],"award-info":[{"award-number":["EB033994"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Although cone beam computed tomography (CBCT) has lower resolution compared to planning CTs (pCT), its lower dose, higher high\u2010contrast resolution, and shorter scanning time support its widespread use in clinical applications, especially in ensuring accurate patient positioning during the image\u2010guided radiation therapy (IGRT) process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>While CBCT is critical to IGRT, CBCT image quality can be compromised by severe stripe and scattering artifacts. Tumor movement secondary to respiratory motion also decreases CBCT resolution. In order to improve the image quality of CBCT, we propose a Lung Diffusion Model (L\u2010DM) framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Our proposed algorithm is based on a conditional diffusion model trained on pCT and deformed CBCT (dCBCT) image pairs to synthesize lung CT images from dCBCT images and benefit CBCT\u2010based radiotherapy. dCBCT images were used as the constraint for the L\u2010DM. The image quality and Hounsfield unit (HU) values of the synthetic CTs (sCT) images generated by the proposed L\u2010DM were compared to three selected mainstream generation models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We verified our model in both an institutional lung cancer dataset and a selected public dataset. Our L\u2010DM showed significant improvement in the four metrics of mean absolute error (MAE), peak signal\u2010to\u2010noise ratio (PSNR), normalized cross\u2010correlation (NCC), and structural similarity index measure (SSIM). In our institutional dataset, our proposed L\u2010DM decreased the MAE from 101.47 to 37.87\u00a0HU and increased the PSNR from 24.97 to 29.89 dB, the NCC from 0.81 to 0.97, and the SSIM from 0.80 to 0.93. In the public dataset, our proposed L\u2010DM decreased the MAE from 173.65 to 58.95\u00a0HU, while increasing the PSNR, NCC, and SSIM from 13.07 to 24.05 dB, 0.68 to 0.94, and 0.41 to 0.88, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed L\u2010DM significantly improved sCT image quality compared to the pre\u2010correction CBCT and\u00a0three mainstream generative models. Our model can benefit CBCT\u2010based IGRT and other potential clinical applications as it increases the HU accuracy and decreases the artifacts from input CBCT images.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17328","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T15:44:35Z","timestamp":1722527075000},"page":"8168-8178","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["CBCT\u2010based synthetic CT image generation using a diffusion model for CBCT\u2010guided lung radiotherapy"],"prefix":"10.1002","volume":"51","author":[{"given":"Xiaoqian","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Winship Cancer Institute Emory University School of Medicine  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard L. 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