{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T16:07:44Z","timestamp":1779034064681,"version":"3.51.4"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007957","name":"Chongqing Municipal Education Commission","doi-asserted-by":"publisher","award":["KJQN202301543"],"award-info":[{"award-number":["KJQN202301543"]}],"id":[{"id":"10.13039\/501100007957","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007957","name":"Chongqing Municipal Education Commission","doi-asserted-by":"publisher","award":["KJQN202301517"],"award-info":[{"award-number":["KJQN202301517"]}],"id":[{"id":"10.13039\/501100007957","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017501","name":"Science-Health Joint Medical Scientific Research Project of Chongqing","doi-asserted-by":"publisher","award":["2026GDRC005"],"award-info":[{"award-number":["2026GDRC005"]}],"id":[{"id":"10.13039\/100017501","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFB4710100"],"award-info":[{"award-number":["2024YFB4710100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing Municipality","doi-asserted-by":"publisher","award":["CSTB2024NSCQ-LZX0043"],"award-info":[{"award-number":["CSTB2024NSCQ-LZX0043"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.bspc.2026.109892","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T17:27:29Z","timestamp":1771867649000},"page":"109892","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["MSRRA-NAF: Multi-Scale Relative Resolution Attention-Guided X-ray 3D Reconstruction via Neural Attenuation Field"],"prefix":"10.1016","volume":"119","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3518-698X","authenticated-orcid":false,"given":"Zhengyang","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0000-1672-3690","authenticated-orcid":false,"given":"Long","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-9481-0714","authenticated-orcid":false,"given":"Wenjie","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0008-3137-305X","authenticated-orcid":false,"given":"Maodan","family":"Nie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-1554-4791","authenticated-orcid":false,"given":"Yuan","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0009-3308-8137","authenticated-orcid":false,"given":"Peng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0000-7413-6988","authenticated-orcid":false,"given":"Peng","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0000-1052-464X","authenticated-orcid":false,"given":"Cong","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-4549-713X","authenticated-orcid":false,"given":"Tengfei","family":"Weng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0004-2895-6546","authenticated-orcid":false,"given":"Zhong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-9423-9105","authenticated-orcid":false,"given":"Changqing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0000-2291-3431","authenticated-orcid":false,"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3264-9571","authenticated-orcid":false,"given":"Qi","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.bspc.2026.109892_b1","doi-asserted-by":"crossref","first-page":"1894","DOI":"10.1109\/TMI.2019.2960720","article-title":"An enhanced SMART-recon algorithm for time-resolved C-arm cone-beam ct imaging","volume":"39","author":"Li","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.bspc.2026.109892_b2","article-title":"Three-dimensional examination of humerus and antebrachium bones in the red hawk (buteo rufinus) with computed tomography (CT)","volume":"35","author":"Delba","year":"2024","journal-title":"Van Vet. J."},{"key":"10.1016\/j.bspc.2026.109892_b3","doi-asserted-by":"crossref","DOI":"10.7759\/cureus.73172","article-title":"Assessing the accuracy of linear alveolar bone measurements for implant planning using cone-beam computed tomography by comparing three competent three-dimensional imaging software: An in vitro study","author":"Neralla","year":"2024","journal-title":"Cureus"},{"issue":"4","key":"10.1016\/j.bspc.2026.109892_b4","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/j.cden.2008.05.005","article-title":"What is cone-beam CT and how does it work?","volume":"52","author":"Scarfe","year":"2008","journal-title":"Dent. Clin. North Am."},{"key":"10.1016\/j.bspc.2026.109892_b5","series-title":"Learning deep intensity field for extremely sparse-view cbct reconstruction","author":"Lin","year":"2023"},{"key":"10.1016\/j.bspc.2026.109892_b6","first-page":"59","article-title":"Cone beam CT paranasal sinuses versus standard multidetector and low dose multidetector CT studies","author":"Al Abduwani","year":"2016","journal-title":"Am. J. Otolaryngology\u2013Head Neck Med. Surg."},{"key":"10.1016\/j.bspc.2026.109892_b7","doi-asserted-by":"crossref","DOI":"10.1155\/2014\/982695","article-title":"3D alternating direction TV-based cone-beam CT reconstruction with efficient GPU implementation","volume":"2014","author":"Cai","year":"2014","journal-title":"Comput. Math. Methods Med."},{"key":"10.1016\/j.bspc.2026.109892_b8","doi-asserted-by":"crossref","unstructured":"X. Ying, H. Guo, K. Ma, J. Wu, Z. Weng, Y. Zheng, X2CT-GAN: reconstructing CT from biplanar X-rays with generative adversarial networks, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 10619\u201310628.","DOI":"10.1109\/CVPR.2019.01087"},{"key":"10.1016\/j.bspc.2026.109892_b9","doi-asserted-by":"crossref","unstructured":"R. Anirudh, H. Kim, J.J. Thiagarajan, K.A. Mohan, K. Champley, T. Bremer, Lose the views: Limited angle CT reconstruction via implicit sinogram completion, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6343\u20136352.","DOI":"10.1109\/CVPR.2018.00664"},{"key":"10.1016\/j.bspc.2026.109892_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107424","article-title":"Investigation on super-resolution reconstruction of lung CT images for COVID-19 based on sequential images","volume":"102","author":"Zhang","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.109892_b11","series-title":"Machine Learning for Medical Image Reconstruction: Third International Workshop, MLMIR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings 3","first-page":"123","article-title":"End-to-end convolutional neural network for 3D reconstruction of knee bones from bi-planar X-ray images","author":"Kasten","year":"2020"},{"key":"10.1016\/j.bspc.2026.109892_b12","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"13","article-title":"Learning deep intensity field for extremely sparse-view cbct reconstruction","author":"Lin","year":"2023"},{"key":"10.1016\/j.bspc.2026.109892_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104868","article-title":"Deep residual constrained reconstruction via learned convolutional sparse coding for low-dose CT imaging","volume":"85","author":"Liu","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.109892_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103598","article-title":"An unsupervised reconstruction method for low-dose CT using deep generative regularization prior","volume":"75","author":"Unal","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"issue":"1","key":"10.1016\/j.bspc.2026.109892_b15","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1145\/3503250","article-title":"Nerf: Representing scenes as neural radiance fields for view synthesis","volume":"65","author":"Mildenhall","year":"2021","journal-title":"Commun. ACM"},{"key":"10.1016\/j.bspc.2026.109892_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.127063","article-title":"Roformer: Enhanced transformer with rotary position embedding","volume":"568","author":"Su","year":"2024","journal-title":"Neurocomputing"},{"issue":"4","key":"10.1016\/j.bspc.2026.109892_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3528223.3530127","article-title":"Instant neural graphics primitives with a multiresolution hash encoding","volume":"41","author":"M\u00fcller","year":"2022","journal-title":"ACM Trans. Graph."},{"issue":"6","key":"10.1016\/j.bspc.2026.109892_b18","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1364\/JOSAA.1.000612","article-title":"Practical cone-beam algorithm. j opt soc am a 1:612-619","volume":"1","author":"Feldkamp","year":"1984","journal-title":"J. Opt. Soc. Amer. A"},{"issue":"1","key":"10.1016\/j.bspc.2026.109892_b19","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1177\/016173468400600107","article-title":"Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm","volume":"6","author":"Andersen","year":"1984","journal-title":"Ultrason. Imaging"},{"issue":"17","key":"10.1016\/j.bspc.2026.109892_b20","doi-asserted-by":"crossref","first-page":"4777","DOI":"10.1088\/0031-9155\/53\/17\/021","article-title":"Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization","volume":"53","author":"Sidky","year":"2008","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.bspc.2026.109892_b21","doi-asserted-by":"crossref","unstructured":"H. Chung, D. Ryu, M.T. McCann, M.L. Klasky, J.C. Ye, Solving 3D Inverse Problems Using Pre-Trained 2D Diffusion Models, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2023, pp. 22542\u201322551.","DOI":"10.1109\/CVPR52729.2023.02159"},{"key":"10.1016\/j.bspc.2026.109892_b22","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"442","article-title":"NAF: neural attenuation fields for sparse-view CBCT reconstruction","author":"Zha","year":"2022"},{"key":"10.1016\/j.bspc.2026.109892_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.cag.2023.11.005","article-title":"Efficient ray sampling for radiance fields reconstruction","author":"Sun","year":"2024","journal-title":"Comput. Graph."},{"key":"10.1016\/j.bspc.2026.109892_b24","doi-asserted-by":"crossref","unstructured":"Y. Cai, J. Wang, A. Yuille, Z. Zhou, A. Wang, Structure-aware sparse-view x-ray 3d reconstruction, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 11174\u201311183.","DOI":"10.1109\/CVPR52733.2024.01062"},{"key":"10.1016\/j.bspc.2026.109892_b25","series-title":"2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society","first-page":"3843","article-title":"Mednerf: Medical neural radiance fields for reconstructing 3d-aware ct-projections from a single x-ray","author":"Corona-Figueroa","year":"2022"},{"issue":"4","key":"10.1016\/j.bspc.2026.109892_b26","first-page":"1","article-title":"Neat: Neural adaptive tomography","volume":"41","author":"R\u00fcckert","year":"2022","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.bspc.2026.109892_b27","doi-asserted-by":"crossref","unstructured":"G. Zang, R. Idoughi, R. Li, P. Wonka, W. Heidrich, Intratomo: self-supervised learning-based tomography via sinogram synthesis and prediction, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 1960\u20131970.","DOI":"10.1109\/ICCV48922.2021.00197"},{"key":"10.1016\/j.bspc.2026.109892_b28","series-title":"European Conference on Computer Vision","first-page":"333","article-title":"Tensorf: Tensorial radiance fields","author":"Chen","year":"2022"},{"issue":"2","key":"10.1016\/j.bspc.2026.109892_b29","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1118\/1.3528204","article-title":"The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on ct scans","volume":"38","author":"Armato III","year":"2011","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2026.109892_b30","series-title":"Scientific visualization datasets","author":"Klacansky.","year":"2022"},{"issue":"5","key":"10.1016\/j.bspc.2026.109892_b31","doi-asserted-by":"crossref","DOI":"10.1088\/2057-1976\/2\/5\/055010","article-title":"TIGRE: a MATLAB-GPU toolbox for CBCT image reconstruction","volume":"2","author":"Biguri","year":"2016","journal-title":"Biomed. Phys. Eng. Express"},{"key":"10.1016\/j.bspc.2026.109892_b32","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.109892_b33","series-title":"International Conference on Learning Representations","first-page":"6","article-title":"A method for stochastic optimization","volume":"5","author":"Kinga","year":"2015"},{"issue":"4","key":"10.1016\/j.bspc.2026.109892_b34","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.bspc.2026.109892_b35","article-title":"Contourlet and discrete cosine transform based quality guaranteed robust image watermarking method using artificial bee colony algorithm","volume":"212","author":"Gul","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.109892_b36","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1200\/CCI.19.00068","article-title":"Quantitative assessment of the effects of compression on deep learning in digital pathology image analysis.","volume":"4","author":"Chen","year":"2020","journal-title":"JCO Clin. Cancer Informatics"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S1746809426004465?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S1746809426004465?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T15:49:15Z","timestamp":1779032955000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426004465"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":36,"alternative-id":["S1746809426004465"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.bspc.2026.109892","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MSRRA-NAF: Multi-Scale Relative Resolution Attention-Guided X-ray 3D Reconstruction via Neural Attenuation Field","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.bspc.2026.109892","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109892"}}