{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T22:24:18Z","timestamp":1793139858409,"version":"4.5.0"},"reference-count":61,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013365","name":"West China Hospital of Sichuan University","doi-asserted-by":"publisher","award":["20HXJS040"],"award-info":[{"award-number":["20HXJS040"]}],"id":[{"id":"10.13039\/501100013365","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004912","name":"Sichuan University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004912","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004829","name":"Science and Technology Department of Sichuan Province","doi-asserted-by":"publisher","award":["RZHZ2022008"],"award-info":[{"award-number":["RZHZ2022008"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1016\/j.compmedimag.2024.102344","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T12:23:54Z","timestamp":1706876634000},"page":"102344","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":21,"special_numbering":"C","title":["Energy-guided diffusion model for CBCT-to-CT synthesis"],"prefix":"10.1016","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9109-2670","authenticated-orcid":false,"given":"Linjie","family":"Fu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuding","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6416-9516","authenticated-orcid":false,"given":"Yali","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2024.102344_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2019.101684","article-title":"MedGAN: Medical image translation using GANs","volume":"79","author":"Armanious","year":"2020","journal-title":"Comput. Med. Imaging Graphics"},{"issue":"10","key":"10.1016\/j.compmedimag.2024.102344_b2","doi-asserted-by":"crossref","first-page":"4683","DOI":"10.1002\/mp.14387","article-title":"Comparison of CBCT-based dose calculation methods in head and neck cancer radiotherapy: From hounsfield unit to density calibration curve to deep learning","volume":"47","author":"Barateau","year":"2020","journal-title":"Med. Phys."},{"key":"10.1016\/j.compmedimag.2024.102344_b3","first-page":"43","article-title":"Synthesis of positron emission tomography (PET) images via multi-channel generative adversarial networks (GANs)","author":"Bi","year":"2017"},{"issue":"6","key":"10.1016\/j.compmedimag.2024.102344_b4","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1120\/jacmp.v13i6.4023","article-title":"The impact of continuously-variable dose rate VMAT on beam stability, MLC positioning, and overall plan dosimetry","volume":"13","author":"Boylan","year":"2012","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"10.1016\/j.compmedimag.2024.102344_b5","series-title":"Simulation and Synthesis in Medical Imaging: Second International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Qu\u00c9Bec City, QC, Canada, September 10, 2017, Proceedings 2","first-page":"3","article-title":"Adversarial image synthesis for unpaired multi-modal cardiac data","author":"Chartsias","year":"2017"},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102344_b6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3857\/roj.2018.00122","article-title":"Intensity-modulated radiation therapy: A review with a physics perspective","volume":"36","author":"Cho","year":"2018","journal-title":"Radiat. Oncol. J."},{"key":"10.1016\/j.compmedimag.2024.102344_b7","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.ejmp.2019.05.005","article-title":"Impact of plan parameters and modulation indices on patient-specific QA results for standard and stereotactic VMAT","volume":"62","author":"Chun","year":"2019","journal-title":"Phys. Medica"},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102344_b8","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1109\/TMI.2022.3220681","article-title":"MR image denoising and super-resolution using regularized reverse diffusion","volume":"42","author":"Chung","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2024.102344_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102479","article-title":"Score-based diffusion models for accelerated MRI","volume":"80","author":"Chung","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2024.102344_b10","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2023.3261988","article-title":"Diffusion models in vision: A survey","author":"Croitoru","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"10.1016\/j.compmedimag.2024.102344_b11","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TMI.2019.2901750","article-title":"Image synthesis in multi-contrast MRI with conditional generative adversarial networks","volume":"38","author":"Dar","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10.1016\/j.compmedimag.2024.102344_b12","doi-asserted-by":"crossref","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":"10.1016\/j.compmedimag.2024.102344_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106889","article-title":"Synthetic CT generation from CBCT using double-chain-CycleGAN","volume":"161","author":"Deng","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compmedimag.2024.102344_b14","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume":"vol. 34","author":"Dhariwal","year":"2021"},{"key":"10.1016\/j.compmedimag.2024.102344_b15","article-title":"Generative adversarial nets","volume":"vol. 27","author":"Goodfellow","year":"2014"},{"key":"10.1016\/j.compmedimag.2024.102344_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102872","article-title":"Adaptive diffusion priors for accelerated MRI reconstruction","author":"G\u00fcng\u00f6r","year":"2023","journal-title":"Med. Image Anal."},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102344_b17","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1002\/mp.12155","article-title":"MR-based synthetic CT generation using a deep convolutional neural network method","volume":"44","author":"Han","year":"2017","journal-title":"Med. Phys."},{"key":"10.1016\/j.compmedimag.2024.102344_b18","series-title":"Simulation and Synthesis in Medical Imaging: Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings 3","first-page":"31","article-title":"Cross-modality image synthesis from unpaired data using cyclegan: Effects of gradient consistency loss and training data size","author":"Hiasa","year":"2018"},{"key":"10.1016\/j.compmedimag.2024.102344_b19","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"vol. 33","author":"Ho","year":"2020"},{"key":"10.1016\/j.compmedimag.2024.102344_b20","doi-asserted-by":"crossref","unstructured":"Hu, X., Zhou, X., Huang, Q., Shi, Z., Sun, L., Li, Q., 2022. Qs-attn: Query-selected attention for contrastive learning in i2i translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 18291\u201318300.","DOI":"10.1109\/CVPR52688.2022.01775"},{"key":"10.1016\/j.compmedimag.2024.102344_b21","first-page":"14938","article-title":"Robust compressed sensing mri with deep generative priors","volume":"34","author":"Jalal","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2024.102344_b22","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.media.2016.08.009","article-title":"\u2018Random forest regression for magnetic resonance image synthesis\u2019","volume":"35","author":"Jog","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2024.102344_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106803","article-title":"Structure-preserving quality improvement of cone beam CT images using contrastive learning","volume":"158","author":"Kang","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"7Part1","key":"10.1016\/j.compmedimag.2024.102344_b24","doi-asserted-by":"crossref","first-page":"3648","DOI":"10.1118\/1.3438478","article-title":"Kilovoltage cone-beam CT: Comparative dose and image quality evaluations in partial and full-angle scan protocols","volume":"37","author":"Kim","year":"2010","journal-title":"Med. Phys."},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102344_b25","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1016\/j.ijrobp.2017.04.023","article-title":"Online adaptive radiation therapy","volume":"99","author":"Lim-Reinders","year":"2017","journal-title":"Int. J. Radiat. Oncol.* Biol.* Phys."},{"issue":"6","key":"10.1016\/j.compmedimag.2024.102344_b26","doi-asserted-by":"crossref","first-page":"2472","DOI":"10.1002\/mp.14121","article-title":"CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy","volume":"47","author":"Liu","year":"2020","journal-title":"Med. Phys."},{"key":"10.1016\/j.compmedimag.2024.102344_b27","series-title":"Conversion between ct and mri images using diffusion and score-matching models","author":"Lyu","year":"2022"},{"issue":"16","key":"10.1016\/j.compmedimag.2024.102344_b28","doi-asserted-by":"crossref","first-page":"4495","DOI":"10.1088\/0031-9155\/55\/16\/S05","article-title":"Variance reduction techniques for fast Monte Carlo CBCT scatter correction calculations","volume":"55","author":"Mainegra-Hing","year":"2010","journal-title":"Phys. Med. Biol."},{"issue":"3","key":"10.1016\/j.compmedimag.2024.102344_b29","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1118\/1.596088","article-title":"Scatter-glare correction using a convolution algorithm with variable weighting","volume":"14","author":"Naimuddin","year":"1987","journal-title":"Med. Phys."},{"key":"10.1016\/j.compmedimag.2024.102344_b30","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","article-title":"Gradient boosting machines, a tutorial","volume":"7","author":"Natekin","year":"2013","journal-title":"Front. Neurorobot."},{"key":"10.1016\/j.compmedimag.2024.102344_b31","series-title":"Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings 1","first-page":"170","article-title":"Estimating CT image from MRI data using 3D fully convolutional networks","author":"Nie","year":"2016"},{"key":"10.1016\/j.compmedimag.2024.102344_b32","series-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part IX 16","first-page":"319","article-title":"Contrastive learning for unpaired image-to-image translation","author":"Park","year":"2020"},{"key":"10.1016\/j.compmedimag.2024.102344_b33","series-title":"Generating realistic 3D brain MRIs using a conditional diffusion probabilistic model","author":"Peng","year":"2022"},{"key":"10.1016\/j.compmedimag.2024.102344_b34","series-title":"CBCT-based synthetic CT image generation using conditional denoising diffusion probabilistic model","author":"Peng","year":"2023"},{"issue":"5\u20136","key":"10.1016\/j.compmedimag.2024.102344_b35","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1561\/2200000073","article-title":"Computational optimal transport: With applications to data science","volume":"11","author":"Peyr\u00e9","year":"2019","journal-title":"Found. Trends Mach. Learn."},{"key":"10.1016\/j.compmedimag.2024.102344_b36","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"705","article-title":"Fast unsupervised brain anomaly detection and segmentation with diffusion models","author":"Pinaya","year":"2022"},{"key":"10.1016\/j.compmedimag.2024.102344_b37","series-title":"MICCAI Workshop on Deep Generative Models","first-page":"117","article-title":"Brain imaging generation with latent diffusion models","author":"Pinaya","year":"2022"},{"key":"10.1016\/j.compmedimag.2024.102344_b38","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ejmp.2019.02.014","article-title":"A review of cone-beam CT applications for adaptive radiotherapy of prostate cancer","volume":"59","author":"Posiewnik","year":"2019","journal-title":"Phys. Med."},{"key":"10.1016\/j.compmedimag.2024.102344_b39","series-title":"Swish: a self-gated activation function","author":"Ramachandran","year":"2017"},{"issue":"2","key":"10.1016\/j.compmedimag.2024.102344_b40","first-page":"119","article-title":"Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT","volume":"14","author":"Sidky","year":"2006","journal-title":"J. X-ray Sci. Technol."},{"key":"10.1016\/j.compmedimag.2024.102344_b41","article-title":"Generative modeling by estimating gradients of the data distribution","volume":"vol. 32","author":"Song","year":"2019"},{"key":"10.1016\/j.compmedimag.2024.102344_b42","series-title":"Score-based generative modeling through stochastic differential equations","author":"Song","year":"2020"},{"issue":"5","key":"10.1016\/j.compmedimag.2024.102344_b43","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.canrad.2006.05.012","article-title":"Clinical application of image-guided radiotherapy, IGRT (on the varian OBI platform)","volume":"10","author":"Sorcini","year":"2006","journal-title":"Cancer\/Radioth\u00e9rapie"},{"issue":"9","key":"10.1016\/j.compmedimag.2024.102344_b44","doi-asserted-by":"crossref","first-page":"4437","DOI":"10.1002\/mp.12385","article-title":"Optimal combination of anti-scatter grids and software correction for CBCT imaging","volume":"44","author":"Stankovic","year":"2017","journal-title":"Med. Phys."},{"issue":"18","key":"10.1016\/j.compmedimag.2024.102344_b45","doi-asserted-by":"crossref","first-page":"5949","DOI":"10.1088\/0031-9155\/56\/18\/011","article-title":"Low-dose CT reconstruction via edge-preserving total variation regularization","volume":"56","author":"Tian","year":"2011","journal-title":"Phys. Med. Biol."},{"issue":"6Part9","key":"10.1016\/j.compmedimag.2024.102344_b46","doi-asserted-by":"crossref","first-page":"3422","DOI":"10.1118\/1.4955978","article-title":"SU-F-J-70: Monte Carlo study on a cone-beam computed tomography using a cross-type carbon fiber antiscatter grid","volume":"43","author":"Usui","year":"2016","journal-title":"Med. Phys."},{"issue":"7","key":"10.1016\/j.compmedimag.2024.102344_b47","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1118\/1.1584044","article-title":"The value of scatter removal by a grid in full field digital mammography","volume":"30","author":"Veldkamp","year":"2003","journal-title":"Med. Phys."},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102344_b48","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1118\/1.4938267","article-title":"Automated segmentation of dental CBCT image with prior-guided sequential random forests","volume":"43","author":"Wang","year":"2016","journal-title":"Med. Phys."},{"key":"10.1016\/j.compmedimag.2024.102344_b49","series-title":"Zero-shot-learning cross-modality data translation through mutual information guided stochastic diffusion","author":"Wang","year":"2023"},{"key":"10.1016\/j.compmedimag.2024.102344_b50","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhou, W., Bao, J., Chen, D., Li, H., 2021. Instance-wise hard negative example generation for contrastive learning in unpaired image-to-image translation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 14020\u201314029.","DOI":"10.1109\/ICCV48922.2021.01376"},{"key":"10.1016\/j.compmedimag.2024.102344_b51","series-title":"International Conference on Medical Imaging with Deep Learning","first-page":"1336","article-title":"Diffusion models for implicit image segmentation ensembles","author":"Wolleb","year":"2022"},{"key":"10.1016\/j.compmedimag.2024.102344_b52","doi-asserted-by":"crossref","unstructured":"Wu, Y., He, K., 2018. Group normalization. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 3\u201319.","DOI":"10.1007\/978-3-030-01261-8_1"},{"issue":"3","key":"10.1016\/j.compmedimag.2024.102344_b53","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1097\/PPO.0b013e31821da9d8","article-title":"Adaptive radiation therapy: Technical components and clinical applications","volume":"17","author":"Wu","year":"2011","journal-title":"Cancer J."},{"key":"10.1016\/j.compmedimag.2024.102344_b54","series-title":"Brain pet synthesis from mri using joint probability distribution of diffusion model at ultrahigh fields","author":"Xie","year":"2022"},{"issue":"9","key":"10.1016\/j.compmedimag.2024.102344_b55","doi-asserted-by":"crossref","first-page":"3567","DOI":"10.1088\/0031-9155\/60\/9\/3567","article-title":"A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy","volume":"60","author":"Xu","year":"2015","journal-title":"Phys. Med. Biol."},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102344_b56","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1088\/0031-9155\/42\/1\/008","article-title":"Adaptive radiation therapy","volume":"42","author":"Yan","year":"1997","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.compmedimag.2024.102344_b57","doi-asserted-by":"crossref","unstructured":"Yi, Z., Zhang, H., Tan, P., Gong, M., 2017. Dualgan: Unsupervised dual learning for image-to-image translation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2849\u20132857.","DOI":"10.1109\/ICCV.2017.310"},{"key":"10.1016\/j.compmedimag.2024.102344_b58","doi-asserted-by":"crossref","unstructured":"Zhan, F., Zhang, J., Yu, Y., Wu, R., Lu, S., 2022. Modulated contrast for versatile image synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 18280\u201318290.","DOI":"10.1109\/CVPR52688.2022.01774"},{"key":"10.1016\/j.compmedimag.2024.102344_b59","article-title":"Breath-hold CBCT-guided CBCT-to-CT synthesis via multimodal unsupervised representation disentanglement learning","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"10.1016\/j.compmedimag.2024.102344_b60","doi-asserted-by":"crossref","DOI":"10.1118\/1.4825097","article-title":"A technique for estimating 4D-CBCT using prior knowledge and limited-angle projections","volume":"40","author":"Zhang","year":"2013","journal-title":"Med. Phys."},{"key":"10.1016\/j.compmedimag.2024.102344_b61","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P., Efros, A.A., 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2223\u20132232.","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611124000211?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611124000211?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T05:34:29Z","timestamp":1760247269000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611124000211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":61,"alternative-id":["S0895611124000211"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102344","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2024,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Energy-guided diffusion model for CBCT-to-CT synthesis","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102344","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"102344"}}