{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T18:06:44Z","timestamp":1779386804815,"version":"3.53.1"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271335"],"award-info":[{"award-number":["62271335"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2025ZNSFSC0470"],"award-info":[{"award-number":["2025ZNSFSC0470"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014717","name":"National Natural Science Foundation of China National Outstanding Youth Science Fund Project","doi-asserted-by":"publisher","award":["62471148"],"award-info":[{"award-number":["62471148"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014717","name":"National Natural Science Foundation of China National Outstanding Youth Science Fund Project","doi-asserted-by":"publisher","award":["62176059"],"award-info":[{"award-number":["62176059"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.patcog.2026.113513","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T14:44:44Z","timestamp":1773758684000},"page":"113513","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Universal pre-training for generalizable incomplete-view CT reconstruction"],"prefix":"10.1016","volume":"178","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1632-2480","authenticated-orcid":false,"given":"Chenglong","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1955-3508","authenticated-orcid":false,"given":"Zilong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1813-1784","authenticated-orcid":false,"given":"Junjun","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5924-3360","authenticated-orcid":false,"given":"Junping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7201-2092","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0604-3197","authenticated-orcid":false,"given":"Hongming","family":"Shan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.patcog.2026.113513_bib0001","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1118\/1.2836950","article-title":"An outlook on X-ray CT research and development","volume":"35","author":"Wang","year":"2008","journal-title":"Med. Phys."},{"key":"10.1016\/j.patcog.2026.113513_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111233","article-title":"A dual-domain deep network for high pitch CT reconstruction","volume":"161","author":"Wang","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113513_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108150","article-title":"4D computed tomography super-resolution reconstruction based on tensor product and nuclear norm optimization","volume":"121","author":"Zhang","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113513_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111285","article-title":"Self-supervised noise2noise method utilizing corrupted images with a modular network for LDCT denoising","volume":"161","author":"Zhu","year":"2025","journal-title":"Pattern Recognit."},{"issue":"7","key":"10.1016\/j.patcog.2026.113513_bib0005","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1088\/0031-9155\/58\/7\/2119","article-title":"A limited-angle CT reconstruction method based on anisotropic TV minimization","volume":"58","author":"Chen","year":"2013","journal-title":"Phys. Med. Biol."},{"issue":"23","key":"10.1016\/j.patcog.2026.113513_bib0006","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ab9771","article-title":"A sequential regularization based image reconstruction method for limited-angle spectral CT","volume":"65","author":"Sheng","year":"2020","journal-title":"Phys. Med. Biol."},{"issue":"6","key":"10.1016\/j.patcog.2026.113513_bib0007","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1109\/TMI.2018.2823768","article-title":"Framing U-Net via deep convolutional framelets: application to sparse-view CT","volume":"37","author":"Han","year":"2018","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.patcog.2026.113513_bib0008","series-title":"Int. Congr. Ser.","first-page":"713","article-title":"Practical model 3DX of limited cone-beam X-ray CT for dental use","volume":"Vol. 1230","author":"Arai","year":"2001"},{"issue":"5","key":"10.1016\/j.patcog.2026.113513_bib0009","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1118\/1.4944785","article-title":"The rotate-plus-shift C-arm trajectory. Part I. Complete data with less than 180\u00b0 rotation","volume":"43","author":"Ritschl","year":"2016","journal-title":"Med. Phys."},{"issue":"16","key":"10.1016\/j.patcog.2026.113513_bib0010","doi-asserted-by":"crossref","DOI":"10.1088\/0031-9155\/58\/16\/R161","article-title":"The meaning of interior tomography","volume":"58","author":"Wang","year":"2013","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.patcog.2026.113513_bib0011","series-title":"ACM MM","first-page":"2645","article-title":"Learning projection views for sparse-view CT reconstruction","author":"Yang","year":"2022"},{"key":"10.1016\/j.patcog.2026.113513_bib0012","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"10512","article-title":"DuDoNet: dual domain network for CT metal artifact reduction","author":"Lin","year":"2019"},{"issue":"6","key":"10.1016\/j.patcog.2026.113513_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2022.100498","article-title":"Multi-domain integrative Swin transformer network for sparse-view tomographic reconstruction","volume":"3","author":"Pan","year":"2022","journal-title":"Patterns"},{"key":"10.1016\/j.patcog.2026.113513_bib0014","unstructured":"Y. Liu, R. Ge, Y. He, Z. Wu, C. You, S. Li, Y. Chen, Imaging foundation model for universal enhancement of non-ideal measurement CT, (2024). arXiv preprint arXiv: 2410.01591."},{"issue":"11","key":"10.1016\/j.patcog.2026.113513_bib0015","doi-asserted-by":"crossref","first-page":"3002","DOI":"10.1109\/TMI.2021.3078067","article-title":"DRONE: dual-domain residual-based optimization network for sparse-view CT reconstruction","volume":"40","author":"Wu","year":"2021","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.patcog.2026.113513_bib0016","article-title":"General pre-trained inertial signal feature extraction based on temporal memory fusion","author":"Wang","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.113513_bib0017","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"21196","article-title":"Learning to distill global representation for sparse-view CT","author":"Li","year":"2023"},{"key":"10.1016\/j.patcog.2026.113513_bib0018","unstructured":"J. Gu, J.C. Ye, Multi-scale wavelet domain residual learning for limited-angle CT reconstruction, (2017). arXiv preprint arXiv: 1703.01382."},{"issue":"9","key":"10.1016\/j.patcog.2026.113513_bib0019","doi-asserted-by":"crossref","first-page":"4509","DOI":"10.1109\/TIP.2017.2713099","article-title":"Deep convolutional neural network for inverse problems in imaging","volume":"26","author":"Jin","year":"2017","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"10.1016\/j.patcog.2026.113513_bib0020","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1109\/TMI.2018.2823338","article-title":"A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution","volume":"37","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.patcog.2026.113513_bib0021","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.patcog.2016.09.022","article-title":"RODEO: Robust DE-aliasing autoencOder for real-time medical image reconstruction","volume":"63","author":"Mehta","year":"2017","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113513_bib0022","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"25317","article-title":"QN-Mixer: a Quasi-Newton MLP-Mixer model for sparse-view CT reconstruction","author":"Ayad","year":"2024"},{"issue":"2","key":"10.1016\/j.patcog.2026.113513_bib0023","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1109\/TRPMS.2022.3222213","article-title":"LEARN++: recurrent dual-domain reconstruction network for compressed sensing CT","volume":"7","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Radiat. Plasma. Med. Sci."},{"issue":"1","key":"10.1016\/j.patcog.2026.113513_bib0024","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/JBHI.2022.3225697","article-title":"DREAM-Net: deep residual error iterative minimization network for sparse-view CT reconstruction","volume":"27","author":"Zhang","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"3","key":"10.1016\/j.patcog.2026.113513_bib0025","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1109\/TMI.2014.2380993","article-title":"Sparse-view spectral CT reconstruction using spectral patch-based low-rank penalty","volume":"34","author":"Kim","year":"2015","journal-title":"IEEE Trans. Med. Imag."},{"issue":"5","key":"10.1016\/j.patcog.2026.113513_bib0026","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1109\/TRPMS.2023.3242662","article-title":"CROSS: cross-domain residual-optimization-based structure strengthening reconstruction for limited-angle CT","volume":"7","author":"Hu","year":"2023","journal-title":"IEEE Trans. Radiat. Plasma. Med. Sci."},{"issue":"10","key":"10.1016\/j.patcog.2026.113513_bib0027","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1109\/TMI.2024.3376414","article-title":"Multi-channel optimization generative model for stable ultra-sparse-view CT reconstruction","volume":"43","author":"Wu","year":"2024","journal-title":"IEEE Trans. Med. Imag."},{"issue":"10","key":"10.1016\/j.patcog.2026.113513_bib0028","doi-asserted-by":"crossref","first-page":"3436","DOI":"10.1109\/TMI.2024.3367167","article-title":"Wavelet-inspired multi-channel score-based model for limited-angle CT reconstruction","volume":"43","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.patcog.2026.113513_bib0029","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.113513_bib0030","series-title":"AMIA Annual Symposium Proceedings","first-page":"624","article-title":"DiffusionCT: latent diffusion model for CT image standardization","volume":"2023","author":"Selim","year":"2024"},{"issue":"9","key":"10.1016\/j.patcog.2026.113513_bib0031","doi-asserted-by":"crossref","first-page":"3629","DOI":"10.1109\/TMI.2024.3494271","article-title":"Physics-informed score-based diffusion model for limited-angle reconstruction of cardiac computed tomography","volume":"44","author":"Han","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2026.113513_bib0032","series-title":"SITIS","first-page":"341","article-title":"Tomographic reconstruction from sparse-view and limited-angle data using a generative adversarial network","author":"Ayad","year":"2022"},{"issue":"2","key":"10.1016\/j.patcog.2026.113513_bib0033","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/TRPMS.2024.3471677","article-title":"PrideDiff: physics-regularized generalized diffusion model for CT reconstruction","volume":"9","author":"Lu","year":"2024","journal-title":"IEEE Trans. Radiat. Plasma. Med. Sci."},{"key":"10.1016\/j.patcog.2026.113513_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107167","article-title":"Sparse-view and limited-angle CT reconstruction with untrained networks and deep image prior","volume":"226","author":"Shu","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"issue":"11","key":"10.1016\/j.patcog.2026.113513_bib0035","doi-asserted-by":"crossref","first-page":"3065","DOI":"10.1109\/TMI.2021.3085839","article-title":"CT Reconstruction with PDF: parameter-dependent framework for data from multiple geometries and dose levels","volume":"40","author":"Xia","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2026.113513_bib0036","series-title":"Adv. Neural Inf. Process. Syst.","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020"},{"key":"10.1016\/j.patcog.2026.113513_bib0037","series-title":"Adv. Neural Inf. Process. Syst.","article-title":"Visual prompting via image inpainting","author":"Bar","year":"2022"},{"key":"10.1016\/j.patcog.2026.113513_bib0038","first-page":"21438","article-title":"UniverSeg: universal medical image segmentation","author":"Butoi","year":"2023","journal-title":"Proc. IEEE Int. Conf. Comput. Vis."},{"key":"10.1016\/j.patcog.2026.113513_bib0039","unstructured":"J. Ma, T. Cheng, G. Wang, X. Wang, Q. Zhang, L. Zhang, ProRes: exploring degradation-aware visual prompt for universal image restoration, (2023). arXiv preprint arXiv: 2306.13653."},{"key":"10.1016\/j.patcog.2026.113513_bib0040","article-title":"PromptIR: prompting for all-in-one blind image restoration","author":"Potlapalli","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.113513_bib0041","unstructured":"Z. Li, Y. Lei, C. Ma, J. Zhang, H. Shan, Prompt-in-prompt learning for universal image restoration, (2023). arXiv preprint arXiv: 2312.05038."},{"issue":"12","key":"10.1016\/j.patcog.2026.113513_bib0042","doi-asserted-by":"crossref","DOI":"10.1088\/0266-5611\/29\/12\/125007","article-title":"Characterization and reduction of artifacts in limited angle tomography","volume":"29","author":"Frikel","year":"2013","journal-title":"Inverse Probl."},{"issue":"4","key":"10.1016\/j.patcog.2026.113513_bib0043","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/BF01396611","article-title":"Incomplete data problems in X-ray computerized tomography: II. Truncated projections and region-of-interest tomography","volume":"56","author":"Louis","year":"1989","journal-title":"Numer. Math."},{"key":"10.1016\/j.patcog.2026.113513_bib0044","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"10012","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.patcog.2026.113513_bib0045","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1109\/TIP.2023.3256763","article-title":"Vision transformers for single image dehazing","volume":"32","author":"Song","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.113513_bib0046","series-title":"Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent.","article-title":"FreeSeed: frequency-band-aware and self-guided network for sparse-view CT reconstruction","author":"Ma","year":"2023"},{"key":"10.1016\/j.patcog.2026.113513_bib0047","series-title":"Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent.","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"1","key":"10.1016\/j.patcog.2026.113513_bib0048","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","article-title":"Loss functions for image restoration with neural networks","volume":"3","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Comput. Imaging."},{"issue":"3","key":"10.1016\/j.patcog.2026.113513_bib0049","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.5.3.036501","article-title":"DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning","volume":"5","author":"Yan","year":"2018","journal-title":"J. Med. Imaging"},{"issue":"10","key":"10.1016\/j.patcog.2026.113513_bib0050","doi-asserted-by":"crossref","first-page":"e339","DOI":"10.1002\/mp.12345","article-title":"Low-dose CT for the detection and classification of metastatic liver lesions: results of the 2016 low dose CT grand challenge","volume":"44","author":"McCollough","year":"2017","journal-title":"Med. Phys."},{"key":"10.1016\/j.patcog.2026.113513_bib0051","unstructured":"M. Ronchetti, TorchRadon: fast differentiable routines for computed tomography, (2020). arXiv preprint arXiv: 2009.14788."},{"key":"10.1016\/j.patcog.2026.113513_bib0052","series-title":"MLMIR","first-page":"84","article-title":"DuDoTrans: dual-domain transformer for sparse-view CT reconstruction","author":"Wang","year":"2022"},{"key":"10.1016\/j.patcog.2026.113513_bib0053","first-page":"8024","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.patcog.2026.113513_bib0054","unstructured":"D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, (2014). arXiv preprint arXiv: 1412.6980."},{"issue":"4","key":"10.1016\/j.patcog.2026.113513_bib0055","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."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326004796?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326004796?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:05:32Z","timestamp":1779383132000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326004796"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":55,"alternative-id":["S0031320326004796"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113513","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Universal pre-training for generalizable incomplete-view CT reconstruction","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113513","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":"113513"}}