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However, the presence of severe artifacts and inaccurate Hounsfield unit (HU) values prevent its use for quantitative applications such as organ segmentation and dose calculation. To enable the clinical practice of online ART, it is crucial to obtain CBCT scans with a quality comparable to that of a CT scan.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This work aims to develop a conditional diffusion model to perform image translation from the CBCT to the CT distribution for the image quality improvement of CBCT.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The proposed method is a conditional denoising diffusion probabilistic model (DDPM) that utilizes a time\u2010embedded U\u2010net architecture with residual and attention blocks to gradually transform the white Gaussian noise sample to the target CT distribution conditioned on the CBCT. The model was trained on deformed planning CT (dpCT) and CBCT image pairs, and its feasibility was verified in brain patient study and head\u2010and\u2010neck (H&amp;N) patient study. The performance of the proposed algorithm was evaluated using mean absolute error (MAE), peak signal\u2010to\u2010noise ratio (PSNR) and normalized cross\u2010correlation (NCC) metrics on generated synthetic CT (sCT) samples. The proposed method was also compared to four other diffusion model\u2010based sCT generation methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the brain patient study, the MAE, PSNR, and NCC of the generated sCT were 25.99 HU, 30.49\u00a0dB, and 0.99, respectively, compared to 40.63 HU, 27.87\u00a0dB, and 0.98 of the CBCT images. In the H&amp;N patient study, the metrics were 32.56 HU, 27.65\u00a0dB, 0.98 and 38.99 HU, 27.00, 0.98 for sCT and CBCT, respectively. Compared to the other four diffusion models and one Cycle generative adversarial network (Cycle GAN), the proposed method showed superior results in both visual quality and quantitative analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed conditional DDPM method can generate sCT from CBCT with accurate HU numbers and reduced artifacts, enabling accurate CBCT\u2010based organ segmentation and dose calculation for online ART.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16704","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T12:40:00Z","timestamp":1693399200000},"page":"1847-1859","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":114,"title":["CBCT\u2010Based synthetic CT image generation using conditional denoising diffusion probabilistic model"],"prefix":"10.1002","volume":"51","author":[{"given":"Junbo","family":"Peng","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"},{"name":"Nuclear and Radiological Engineering and Medical physics Programs George W. 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