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It offers real time anatomical visualization with low acquisition cost and dose. Nevertheless, photon scattering and beam hindrance lead CBCT images to suffer from several artifacts. These involve inaccurate Hounsfield Unit (HU) values, which render a lower reliability towards the dose calculations and adaptive planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Computed tomography (CT), on the contrary, offers better image quality and accurate HU calibration, yet is typically acquired using offline mode and fails to capture the intra\u2010treatment anatomical changes. This renders a need for developing an accurate CBCT to CT synthesis to mitigate the gap in imaging quality in the adaptive radiotherapy workflow.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To cater to this, we propose a novel diffusion based conditional generative model, coined EqDiff\u2010CT, to synthesize high quality CT images from CBCT. EqDiff\u2010CT employs a denoising diffusion probabilistic model (DDPM) to iteratively inject noise and learn latent representations that enable reconstruction of anatomically consistent CT images. A group equivariant conditional U\u2010Net backbone, implemented with e2cnn steerable layers, enforces rotational equivariance (cyclic C4 symmetry), helping preserve fine structural details while minimizing noise and artifacts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The system was trained and validated on the SynthRAD2025 dataset, comprising CBCT and CT scans across multiple head and neck anatomical sites, and we compared it with advanced methods such as CycleGAN and DDPM. EqDiff\u2010CT provided substantial gains in structural fidelity, HU accuracy and quantitative metrics. Visual findings further confirm the improved recovery, sharper soft tissue boundaries, and realistic bone reconstructions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The findings suggest that the diffusion model has offered a robust and generalizable framework for CBCT improvements. The proposed solution helps in improving the image quality as well as the clinical confidence in the CBCT guided treatment planning and dose\u00a0calculations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70617","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T03:48:44Z","timestamp":1785815324000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Equivariant conditional diffusion model for head and neck CT image synthesis from CBCT"],"prefix":"10.1002","volume":"53","author":[{"given":"Alzahra","family":"Altalib","sequence":"first","affiliation":[{"name":"School of Science and Engineering University of Dundee Dundee Scotland UK"},{"name":"Department of Applied Medical Sciences Jordan University of Science and Technology Irbid Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Science and Engineering University of Dundee Dundee Scotland UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Perelli","sequence":"additional","affiliation":[{"name":"School of Cardiovascular and Metabolic Health, College of Medicine, Veterinary and Life Sciences University of Glasgow Glasgow Scotland UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2010.06.007"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2013.02.021"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cden.2018.03.002"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1201\/b16465"},{"issue":"2","key":"e_1_2_8_6_1","first-page":"64","article-title":"Cone beam computed tomography\u2010know its secrets","volume":"7","author":"Kumar M","year":"2015","journal-title":"J Int Oral Health"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.594723"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.598574"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3497272"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3130047"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13978"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12155"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1148\/rycan.230011"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12710"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.14499"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15060689"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRPMS.2025.3533749"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2023.100478"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.2478\/pjmpe-2020-0022"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11081435"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15072017"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac7b0a"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15020"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-021-01928-w"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16704"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.14064"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13737"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14121"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15460"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16710"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14987"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15333"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16017"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2023.100512"},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrras.2022.03.009"},{"key":"e_1_2_8_36_1","doi-asserted-by":"publisher","DOI":"10.1177\/15330338211062415"},{"key":"e_1_2_8_37_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16777"},{"key":"e_1_2_8_38_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab7d54"},{"key":"e_1_2_8_39_1","doi-asserted-by":"publisher","DOI":"10.3390\/tomography12050064"},{"key":"e_1_2_8_40_1","unstructured":"LiY ShaoHC LiangX et\u00a0al.CBCT\u2010to\u2010CT Synthesis Via a CT\u2010Domain Frequency\u2010Guided Diffusion Model (FGDM). 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