{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:27:51Z","timestamp":1783024071711,"version":"3.54.6"},"reference-count":54,"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"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110793","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T16:50:50Z","timestamp":1781542250000},"page":"110793","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Patch-tube transformed tensor prior network for dynamic MRI reconstruction"],"prefix":"10.1016","volume":"125","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3291-1282","authenticated-orcid":false,"given":"Hongtao","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxiang","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanghui","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.bspc.2026.110793_b1","doi-asserted-by":"crossref","first-page":"554","DOI":"10.3348\/kjr.2014.15.5.554","article-title":"Perfusion magnetic resonance imaging: A comprehensive update on principles and techniques","volume":"15","author":"Geon-Ho","year":"2014","journal-title":"Korean J. Radiol."},{"key":"10.1016\/j.bspc.2026.110793_b2","article-title":"OCMR (v1.0)\u2013open-access multi-coil k-space dataset for cardiovascular","author":"Chen","year":"2020","journal-title":"Magn. Reson. Imag."},{"key":"10.1016\/j.bspc.2026.110793_b3","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s43657-021-00018-x","article-title":"Recommendation for cardiac magnetic resonance imaging-based phenotypic study: Imaging part","volume":"1","author":"Wang","year":"2021","journal-title":"Phenomics"},{"key":"10.1016\/j.bspc.2026.110793_b4","series-title":"CXPMRG-Bench: Pre-training and benchmarking for X-ray medical report generation on CheXpert plus dataset","author":"Wang","year":"2024"},{"issue":"6","key":"10.1016\/j.bspc.2026.110793_b5","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1002\/mrm.21391","article-title":"Sparse MRI: The application of compressed sensing for rapid MR imaging","volume":"58","author":"Lustig","year":"2007","journal-title":"Magn. Reson. Med."},{"issue":"3","key":"10.1016\/j.bspc.2026.110793_b6","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1002\/mrm.22463","article-title":"Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI","volume":"64","author":"Otazo","year":"2010","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.bspc.2026.110793_b7","series-title":"2024 IEEE International Conference on Image Processing","first-page":"2800","article-title":"Dynamic MRI reconstruction using low-rank plus sparse decomposition with smoothness regularization","author":"Ting","year":"2024"},{"key":"10.1016\/j.bspc.2026.110793_b8","series-title":"Proceedings of the 27th International Conference on International Conference on Machine Learning","first-page":"399","article-title":"Learning fast approximations of sparse coding","author":"Gregor","year":"2010"},{"issue":"12","key":"10.1016\/j.bspc.2026.110793_b9","doi-asserted-by":"crossref","first-page":"3698","DOI":"10.1109\/TMI.2021.3096218","article-title":"Learned low-rank priors in dynamic MR imaging","volume":"40","author":"Ke","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110793_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102190","article-title":"Deep low-rank plus sparse network for dynamic MR imaging","volume":"73","author":"Huang","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110793_b11","series-title":"Motion-Decoupled Spiking Transformer for Audio-Visual Zero-Shot Learning","first-page":"3994","author":"Li","year":"2023"},{"key":"10.1016\/j.bspc.2026.110793_b12","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.laa.2015.07.021","article-title":"Tensor\u2013tensor products with invertible linear transforms","volume":"485","author":"Kernfeld","year":"2015","journal-title":"Linear Algebra Appl."},{"key":"10.1016\/j.bspc.2026.110793_b13","doi-asserted-by":"crossref","first-page":"7233","DOI":"10.1109\/TIP.2020.3000349","article-title":"Framelet representation of tensor nuclear norm for third-order tensor completion","volume":"29","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"10.1016\/j.bspc.2026.110793_b14","doi-asserted-by":"crossref","DOI":"10.1002\/nla.2299","article-title":"Robust tensor completion using transformed tensor singular value decomposition","volume":"27","author":"Song","year":"2020","journal-title":"Numer. Linear Algebra Appl."},{"issue":"3","key":"10.1016\/j.bspc.2026.110793_b15","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1137\/22M1531907","article-title":"A learnable group-tube transform induced tensor nuclear norm and its application for tensor completion","volume":"16","author":"Li","year":"2023","journal-title":"SIAM J. Imag. Sci."},{"issue":"3","key":"10.1016\/j.bspc.2026.110793_b16","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s10915-022-01937-1","article-title":"Nonlinear transform induced tensor nuclear norm for tensor completion","volume":"92","author":"Liu","year":"2022","journal-title":"J. Sci. Comput."},{"issue":"2","key":"10.1016\/j.bspc.2026.110793_b17","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1109\/TNNLS.2021.3104837","article-title":"Dictionary learning with low-rank coding coefficients for tensor completion","volume":"34","author":"Jiang","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"5","key":"10.1016\/j.bspc.2026.110793_b18","doi-asserted-by":"crossref","first-page":"3633","DOI":"10.1109\/TCSVT.2023.3316279","article-title":"Learnable spatial-spectral transform-based tensor nuclear norm for multi-dimensional visual data recovery","volume":"34","author":"Liu","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.bspc.2026.110793_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108034","article-title":"T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstruction","volume":"170","author":"Zhang","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110793_b20","doi-asserted-by":"crossref","unstructured":"Y. Zhang, P. Li, Y. Hu, Dynamic MRI Using Learned Transform-Based Tensor Low-Rank Network (LT2LR-NET), in: 2023 IEEE 20th International Symposium on Biomedical Imaging, ISBI, 2023, pp. 1\u20134.","DOI":"10.1109\/ISBI53787.2023.10230437"},{"key":"10.1016\/j.bspc.2026.110793_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.mri.2025.110337","article-title":"JotlasNet: Joint tensor low-rank and attention-based sparse unrolling network for accelerating dynamic MRI","volume":"118","author":"Zhang","year":"2025","journal-title":"Magn. Reson. Imag."},{"key":"10.1016\/j.bspc.2026.110793_b22","doi-asserted-by":"crossref","unstructured":"X. Wang, H. Gan, UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing, in: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2024, pp. 25149\u201325159.","DOI":"10.1109\/CVPR52733.2024.02376"},{"key":"10.1016\/j.bspc.2026.110793_b23","doi-asserted-by":"crossref","unstructured":"K. Zhang, L. Van Gool, R. Timofte, Deep Unfolding Network for Image Super-Resolution, in: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2020, pp. 3214\u20133223.","DOI":"10.1109\/CVPR42600.2020.00328"},{"issue":"1","key":"10.1016\/j.bspc.2026.110793_b24","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":"2023","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"4","key":"10.1016\/j.bspc.2026.110793_b25","doi-asserted-by":"crossref","first-page":"4915","DOI":"10.1109\/TPAMI.2020.3012955","article-title":"Momentum-Net: Fast and convergent iterative neural network for inverse problems","volume":"45","author":"Chun","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.bspc.2026.110793_b26","first-page":"1314","article-title":"Deep manifold learning for dynamic MR imaging","volume":"7","author":"Ke","year":"2021","journal-title":"IEEE Trans. Comput. Imag."},{"key":"10.1016\/j.bspc.2026.110793_b27","doi-asserted-by":"crossref","unstructured":"J. Zhang, B. Ghanem, ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing, in: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 1828\u20131837.","DOI":"10.1109\/CVPR.2018.00196"},{"key":"10.1016\/j.bspc.2026.110793_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104530","article-title":"Bi-smooth constraints for accelerated dynamic MRI with low-rank plus sparse tensor decomposition","volume":"82","author":"He","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110793_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107364","article-title":"Deep unfolding network with adaptive sequential recovery for MRI reconstruction","volume":"103","author":"Luo","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110793_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.127513","article-title":"A nonlocal feature self-similarity based tensor completion method for video recovery","volume":"580","author":"Lu","year":"2024","journal-title":"Neurocomputing"},{"issue":"3","key":"10.1016\/j.bspc.2026.110793_b31","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.laa.2010.09.020","article-title":"Factorization strategies for third-order tensors","volume":"435","author":"Kilmer","year":"2011","journal-title":"Linear Algebra Appl."},{"key":"10.1016\/j.bspc.2026.110793_b32","doi-asserted-by":"crossref","DOI":"10.1137\/110837711","article-title":"Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging","volume":"34","author":"Kilmer","year":"2013","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"10.1016\/j.bspc.2026.110793_b33","series-title":"Novel methods for multilinear data completion and de-noising based on tensor-SVD","author":"Zhang","year":"2014"},{"key":"10.1016\/j.bspc.2026.110793_b34","first-page":"1","article-title":"Multi-layer probabilistic association reasoning network for image-text retrieval","author":"Li","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.bspc.2026.110793_b35","series-title":"2018 14th IEEE International Conference on Signal Processing","first-page":"1114","article-title":"Dynamic MRI reconstruction using tensor-svd","author":"Ai","year":"2018"},{"key":"10.1016\/j.bspc.2026.110793_b36","article-title":"Dynamic MRI reconstruction via weighted tensor nuclear norm regularizer","volume":"25","author":"Cui","year":"2021","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.bspc.2026.110793_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106848","article-title":"Dynamic MRI reconstruction via multi-directional low-rank tensor regularization","volume":"99","author":"Liu","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"issue":"3","key":"10.1016\/j.bspc.2026.110793_b38","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor decompositions and applications","volume":"51","author":"Kolda","year":"2009","journal-title":"SIAM Rev."},{"issue":"2","key":"10.1016\/j.bspc.2026.110793_b39","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1137\/090761793","article-title":"Rank-sparsity incoherence for matrix decomposition","volume":"21","author":"Chandrasekaran","year":"2011","journal-title":"SIAM J. Optim."},{"issue":"4","key":"10.1016\/j.bspc.2026.110793_b40","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1137\/080738970","article-title":"A singular value thresholding algorithm for matrix completion","volume":"20","author":"Cai","year":"2010","journal-title":"SIAM J. Optim."},{"key":"10.1016\/j.bspc.2026.110793_b41","series-title":"Empirical evaluation of rectified activations in convolutional network","author":"Xu","year":"2015"},{"issue":"9","key":"10.1016\/j.bspc.2026.110793_b42","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1109\/TMI.2012.2203921","article-title":"Image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints","volume":"31","author":"Zhao","year":"2012","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"10.1016\/j.bspc.2026.110793_b43","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1109\/TMI.2010.2100850","article-title":"Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR","volume":"30","author":"Lingala","year":"2011","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.bspc.2026.110793_b44","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TMI.2017.2760978","article-title":"A deep cascade of convolutional neural networks for dynamic MR image reconstruction","volume":"37","author":"Schlemper","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"10.1016\/j.bspc.2026.110793_b45","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1002\/mrm.25240","article-title":"Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components","volume":"73","author":"Otazo","year":"2015","journal-title":"Magn. Reson. Med."},{"issue":"3","key":"10.1016\/j.bspc.2026.110793_b46","first-page":"990","article-title":"ESPIRiT\u2014An eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA","volume":"71","author":"Uecker","year":"2014","journal-title":"Magn. Reson. Imag."},{"issue":"2","key":"10.1016\/j.bspc.2026.110793_b47","first-page":"192","article-title":"NMR phased array","volume":"16","author":"Roemer","year":"1990","journal-title":"Magn. Reson. Imag."},{"key":"10.1016\/j.bspc.2026.110793_b48","series-title":"TensorFlow: A system for large-scale machine learning","author":"Abadi","year":"2016"},{"key":"10.1016\/j.bspc.2026.110793_b49","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2017"},{"issue":"6","key":"10.1016\/j.bspc.2026.110793_b50","first-page":"3055","article-title":"Learning a variational network for reconstruction of accelerated MRI data","volume":"79","author":"Kerstin","year":"2017","journal-title":"Magn. Reson. Imag."},{"issue":"1","key":"10.1016\/j.bspc.2026.110793_b51","first-page":"138","article-title":"CineVN: Variational network reconstruction for rapid functional cardiac cine MRI","volume":"93","author":"Vornehm","year":"2025","journal-title":"Magn. Reson. Imag."},{"key":"10.1016\/j.bspc.2026.110793_b52","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1049\/el:20080522","article-title":"Scope of validity of PSNR in image\/video quality assessment","volume":"44","author":"Huynh-Thu","year":"2008","journal-title":"Electron. Lett."},{"issue":"4","key":"10.1016\/j.bspc.2026.110793_b53","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."},{"issue":"9","key":"10.1016\/j.bspc.2026.110793_b54","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.1109\/TMI.2021.3075856","article-title":"Results of the 2020 fastMRI challenge for machine learning MR image reconstruction","volume":"40","author":"Muckley","year":"2021","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426013479?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426013479?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:10:16Z","timestamp":1783023016000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426013479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":54,"alternative-id":["S1746809426013479"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110793","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Patch-tube transformed tensor prior network for dynamic MRI reconstruction","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110793","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":"110793"}}