{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T03:00:05Z","timestamp":1781060405667,"version":"3.54.1"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Displays"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.displa.2026.103563","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:22:37Z","timestamp":1780356157000},"page":"103563","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Physics-informed cold diffusion for sparse-view CT reconstruction under rapid battery scanning"],"prefix":"10.1016","volume":"95","author":[{"given":"Siyi","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuandong","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruqian","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuan","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufang","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.displa.2026.103563_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.125502","article-title":"Detecting the foreign matter defect in lithium-ion batteries based on battery pilot manufacturing line data analyses","volume":"262","author":"Pan","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.displa.2026.103563_b0010","doi-asserted-by":"crossref","first-page":"2646","DOI":"10.1080\/10589759.2024.2305329","article-title":"Industrial CT image reconstruction for faster scanning through U-Net++ with hybrid attention and loss function","volume":"39","author":"Long","year":"2024","journal-title":"Nondestr. Test. Eval."},{"key":"10.1016\/j.displa.2026.103563_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2025.103169","article-title":"Orthogonal translation computed laminography reconstruction based on self-prior information and adaptive weighted total variation","volume":"90","author":"Tan","year":"2025","journal-title":"Displays"},{"key":"10.1016\/j.displa.2026.103563_b0020","doi-asserted-by":"crossref","first-page":"19117","DOI":"10.1039\/D0RA03602A","article-title":"A gradient screening approach for retired lithium-ion batteries based on X-ray computed tomography images","volume":"10","author":"Ran","year":"2020","journal-title":"RSC Adv."},{"key":"10.1016\/j.displa.2026.103563_b0025","doi-asserted-by":"crossref","first-page":"925","DOI":"10.3390\/en11040925","article-title":"Analysis of manufacturing-induced defects and structural deformations in lithium-ion batteries using computed tomography","volume":"11","author":"Wu","year":"2018","journal-title":"Energies"},{"key":"10.1016\/j.displa.2026.103563_b0030","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1007\/s10921-025-01250-5","article-title":"Exploring image quality improvements in high-speed dual threshold photon-counting micro-CT","volume":"44","author":"Dreier","year":"2025","journal-title":"J. Nondestr. Eval."},{"key":"10.1016\/j.displa.2026.103563_b0035","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.jpowsour.2018.01.087","article-title":"X-ray computed tomography comparison of individual and parallel assembled commercial lithium iron phosphate batteries at end of life after high rate cycling","volume":"381","author":"Carter","year":"2018","journal-title":"J. Power Sources"},{"key":"10.1016\/j.displa.2026.103563_b0040","first-page":"355","article-title":"A curve-filtered FDK (C-FDK) reconstruction algorithm for circular cone-beam CT","volume":"19","author":"Li","year":"2011","journal-title":"J. X-Ray Sci. Technol."},{"key":"10.1016\/j.displa.2026.103563_b0045","first-page":"32241","article-title":"Gold nanoparticles as a potential cellular probe for tracking of stem cells in bone regeneration using dual-energy computed tomography","volume":"8","author":"Wan","year":"2016","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.displa.2026.103563_b0050","doi-asserted-by":"crossref","first-page":"17072","DOI":"10.1364\/OE.522097","article-title":"Reconstruction method suitable for fast CT imaging","volume":"32","author":"Sun","year":"2024","journal-title":"Opt. Express"},{"key":"10.1016\/j.displa.2026.103563_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2024.102734","article-title":"Image fast reconstruction for sparse view computed tomography with reduced sampling integration time","volume":"83","author":"Long","year":"2024","journal-title":"Displays"},{"key":"10.1016\/j.displa.2026.103563_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2025.103073","article-title":"Non-uniform sparse scanning angle selection method for limited angle industrial CT detection of laminated cells","volume":"89","author":"Zhou","year":"2025","journal-title":"Displays"},{"key":"10.1016\/j.displa.2026.103563_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.sna.2025.117318","article-title":"A fast iterative reconstruction method of sparse angle CT for cylindrical lithium battery","volume":"398","author":"Tan","year":"2026","journal-title":"Sens. Actuators A Phys."},{"key":"10.1016\/j.displa.2026.103563_b0070","doi-asserted-by":"crossref","first-page":"e4697","DOI":"10.1002\/cpe.4697","article-title":"Fast parallel image reconstruction for cone\u2010beam FDK algorithm","volume":"31","author":"Zhang","year":"2019","journal-title":"Concurrency Computat. Pract. Exper."},{"key":"10.1016\/j.displa.2026.103563_b0075","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inform. Theory"},{"key":"10.1016\/j.displa.2026.103563_b0080","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.displa.2026.103563_b0085","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."},{"key":"10.1016\/j.displa.2026.103563_b0090","doi-asserted-by":"crossref","first-page":"1682","DOI":"10.1109\/TMI.2012.2195669","article-title":"Low-dose X-ray CT reconstruction via dictionary learning","volume":"31","author":"Xu","year":"2012","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103563_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.sigpro.2020.107871","article-title":"Weighted adaptive non-local dictionary for low-dose CT reconstruction","volume":"180","author":"Yu","year":"2021","journal-title":"Signal Process."},{"key":"10.1016\/j.displa.2026.103563_b0100","doi-asserted-by":"crossref","first-page":"116961","DOI":"10.1109\/ACCESS.2020.3004174","article-title":"Smoothed L0-constraint dictionary learning for low-dose X-Ray CT reconstruction","volume":"8","author":"Komolafe","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.displa.2026.103563_b0105","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1109\/TMI.2018.2878226","article-title":"Non-local low-rank cube-based tensor factorization for spectral CT reconstruction","volume":"38","author":"Wu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103563_b0110","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1016\/j.ejmp.2016.05.063","article-title":"Few-view CT reconstruction via a novel non-local means algorithm","volume":"32","author":"Chen","year":"2016","journal-title":"Phys. Med."},{"key":"10.1016\/j.displa.2026.103563_b0115","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."},{"key":"10.1016\/j.displa.2026.103563_b0120","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. Imaging"},{"key":"10.1016\/j.displa.2026.103563_b0125","doi-asserted-by":"crossref","first-page":"2524","DOI":"10.1109\/TMI.2017.2715284","article-title":"Low-dose CT with a residual encoder-decoder convolutional neural network","volume":"36","author":"Chen","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103563_b0130","doi-asserted-by":"crossref","first-page":"2536","DOI":"10.1109\/TMI.2017.2708987","article-title":"Generative adversarial networks for noise reduction in low-dose CT","volume":"36","author":"Wolterink","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103563_b0135","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1109\/TMI.2018.2827462","article-title":"Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss","volume":"37","author":"Yang","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103563_b0140","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.ejmp.2020.11.021","article-title":"Sparse-view CT reconstruction based on multi-level wavelet convolution neural network","volume":"80","author":"Lee","year":"2020","journal-title":"Phys. Med."},{"key":"10.1016\/j.displa.2026.103563_b0145","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1109\/TCI.2022.3207351","article-title":"DDPTransformer: dual-domain with parallel transformer network for sparse view CT image reconstruction","volume":"8","author":"Li","year":"2022","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"10.1016\/j.displa.2026.103563_b0150","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.displa.2026.103563_b0155","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. Imaging"},{"key":"10.1016\/j.displa.2026.103563_b0160","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/TMI.2018.2805692","article-title":"LEARN: learned experts' assessment-based reconstruction network for sparse-data CT","volume":"37","author":"Chen","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103563_b0165","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1109\/TMI.2021.3054167","article-title":"FISTA-Net: learning a fast iterative shrinkage thresholding network for inverse problems in imaging","volume":"40","author":"Xiang","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103563_b0170","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/TRPMS.2023.3281148","article-title":"RegFormer: a local\u2013nonlocal regularization-based model for sparse-view CT reconstruction","volume":"8","author":"Xia","year":"2024","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"10.1016\/j.displa.2026.103563_b0175","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TMI.2022.3148110","article-title":"DIOR: deep iterative optimization-based residual-learning for limited-angle CT reconstruction","volume":"41","author":"Hu","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103563_b0180","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/TIP.2024.3351382","article-title":"Iterative residual optimization network for limited-angle tomographic reconstruction","volume":"33","author":"Pan","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.displa.2026.103563_b0185","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.displa.2026.103563_b0190","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","author":"Nichol","year":"2021","journal-title":"Proc. Int. Conf. Mach. Learn."},{"key":"10.1016\/j.displa.2026.103563_b0195","first-page":"14347","article-title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","author":"Choi","year":"2021","journal-title":"Proc. IEEE Int. Conf. Comput. vis."},{"key":"10.1016\/j.displa.2026.103563_b0200","unstructured":"W. Xia, Q. Lyu, G. Wang, Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20$\\times $ Speedup, arXiv preprint arXiv:2209. (2022) 15136."},{"key":"10.1016\/j.displa.2026.103563_b0205","first-page":"5775","article-title":"Dpm-solver: a fast ode solver for diffusion probabilistic model sampling in around 10 steps","volume":"35","author":"Lu","year":"2022","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"10.1016\/j.displa.2026.103563_b0210","doi-asserted-by":"crossref","first-page":"41259","DOI":"10.52202\/075280-1789","article-title":"Cold diffusion: Inverting arbitrary image transforms without noise","volume":"36","author":"Bansal","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.displa.2026.103563_b0215","first-page":"745","article-title":"CoreDiff: contextual error-modulated generalized diffusion model for low-dose CT denoising and generalization","volume":"43","author":"Gao","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.displa.2026.103563_b0220","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":"2025","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"10.1016\/j.displa.2026.103563_b0225","article-title":"Generative modeling by estimating gradients of the data distribution","volume":"32","author":"Song","year":"2019","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"10.1016\/j.displa.2026.103563_b0230","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","author":"Sohl-Dickstein","year":"2015","journal-title":"Proc. Int. Conf. Mach. Learn."},{"key":"10.1016\/j.displa.2026.103563_b0235","doi-asserted-by":"crossref","first-page":"2916","DOI":"10.1002\/mp.14170","article-title":"AirNet: fused analytical and iterative reconstruction with deep neural network regularization for sparse\u2010data CT","volume":"47","author":"Chen","year":"2020","journal-title":"Med. Phys."},{"key":"10.1016\/j.displa.2026.103563_b0240","unstructured":"W. Xia, C. Niu, W. Cong, G. Wang, \u201cCube-based 3D denoising diffusion probabilistic model for cone beam computed tomography reconstruction with incomplete data,\u201d arXiv preprint arXiv:2303 (2023).12861."},{"key":"10.1016\/j.displa.2026.103563_b0245","doi-asserted-by":"crossref","first-page":"4294","DOI":"10.1038\/s41467-020-18147-8","article-title":"Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning","volume":"11","author":"Song","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.displa.2026.103563_b0250","doi-asserted-by":"crossref","first-page":"9935","DOI":"10.1038\/s41467-024-53993-w","article-title":"Automated estimation of cementitious sorptivity via computer vision","volume":"15","author":"Kabir","year":"2024","journal-title":"Nat. Commun."}],"container-title":["Displays"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S014193822600226X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S014193822600226X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T02:43:35Z","timestamp":1781059415000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S014193822600226X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":50,"alternative-id":["S014193822600226X"],"URL":"https:\/\/doi.org\/10.1016\/j.displa.2026.103563","relation":{},"ISSN":["0141-9382"],"issn-type":[{"value":"0141-9382","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Physics-informed cold diffusion for sparse-view CT reconstruction under rapid battery scanning","name":"articletitle","label":"Article Title"},{"value":"Displays","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.displa.2026.103563","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103563"}}