{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T12:06:32Z","timestamp":1785153992329,"version":"3.55.0"},"reference-count":57,"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\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012576","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"publisher","award":["2025A1515012208"],"award-info":[{"award-number":["2025A1515012208"]}],"id":[{"id":"10.13039\/501100012576","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012576","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"publisher","award":["2024A1515011697"],"award-info":[{"award-number":["2024A1515011697"]}],"id":[{"id":"10.13039\/501100012576","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012576","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"publisher","award":["2023A1515011644"],"award-info":[{"award-number":["2023A1515011644"]}],"id":[{"id":"10.13039\/501100012576","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":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110974","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T06:51:02Z","timestamp":1783666262000},"page":"110974","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["Edge-prior guided deep unfolding network with multi-scale thresholding for magnetic resonance imaging reconstruction"],"prefix":"10.1016","volume":"126","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5736-5268","authenticated-orcid":false,"given":"Ziyi","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6138-858X","authenticated-orcid":false,"given":"Yujie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3010-329X","authenticated-orcid":false,"given":"Yuping","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6761-8767","authenticated-orcid":false,"given":"Guanghui","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3968-9725","authenticated-orcid":false,"given":"Yu","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1727-4078","authenticated-orcid":false,"given":"Shun","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110974_b1","series-title":"Letter from incoming editor-in-chief","author":"McDermid","year":"2013"},{"issue":"3","key":"10.1016\/j.bspc.2026.110974_b2","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1002\/jmri.28472","article-title":"External hardware and sensors, for improved MRI","volume":"57","author":"Madore","year":"2023","journal-title":"J. Magn. Reson. Imaging"},{"issue":"3","key":"10.1016\/j.bspc.2026.110974_b3","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1002\/nbm.1046","article-title":"An introduction to coil array design for parallel MRI","volume":"19","author":"Ohliger","year":"2006","journal-title":"NMR Biomed.: An Int. J. Devoted To Dev. Appl. Magn. Reson. Vivo"},{"issue":"2","key":"10.1016\/j.bspc.2026.110974_b4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2007.914731","article-title":"An introduction to compressive sampling","volume":"25","author":"Cand\u00e8s","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"issue":"1","key":"10.1016\/j.bspc.2026.110974_b5","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1002\/gamm.201310005","article-title":"Theory and applications of compressed sensing","volume":"36","author":"Kutyniok","year":"2013","journal-title":"GAMM-Mitt."},{"key":"10.1016\/j.bspc.2026.110974_b6","series-title":"2014 IEEE International Conference on Multimedia and Expo","first-page":"1","article-title":"Image compressive-sensing recovery using structured laplacian sparsity in DCT domain and multi-hypothesis prediction","author":"Zhao","year":"2014"},{"issue":"6","key":"10.1016\/j.bspc.2026.110974_b7","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1109\/TCSVT.2016.2527181","article-title":"Video compressive sensing reconstruction via reweighted residual sparsity","volume":"27","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"6","key":"10.1016\/j.bspc.2026.110974_b8","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1109\/TMI.2013.2255133","article-title":"Blind compressive sensing dynamic MRI","volume":"32","author":"Lingala","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.bspc.2026.110974_b9","doi-asserted-by":"crossref","first-page":"5552","DOI":"10.1038\/srep05552","article-title":"Imaging with nature: Compressive imaging using a multiply scattering medium","volume":"4","author":"Liutkus","year":"2014","journal-title":"Sci. Rep."},{"issue":"9","key":"10.1016\/j.bspc.2026.110974_b10","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1109\/81.948437","article-title":"Generalizations of the sampling theorem: Seven decades after nyquist","volume":"48","author":"Vaidyanathan","year":"2002","journal-title":"IEEE Trans. Circuits Syst. I"},{"key":"10.1016\/j.bspc.2026.110974_b11","series-title":"2016 Data Compression Conference","first-page":"161","article-title":"Nonconvex lp nuclear norm based ADMM framework for compressed sensing","author":"Zhao","year":"2016"},{"issue":"9","key":"10.1016\/j.bspc.2026.110974_b12","doi-asserted-by":"crossref","first-page":"4371","DOI":"10.1109\/JBHI.2022.3143104","article-title":"Undersampled multi-contrast MRI reconstruction based on double-domain generative adversarial network","volume":"26","author":"Wei","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"11","key":"10.1016\/j.bspc.2026.110974_b13","doi-asserted-by":"crossref","first-page":"5506","DOI":"10.1109\/JBHI.2023.3311189","article-title":"Frequency learning via multi-scale fourier transformer for mri reconstruction","volume":"27","author":"Yi","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110974_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104632","article-title":"Rnlfnet: Residual non-local Fourier network for undersampled mri reconstruction","volume":"83","author":"Zhou","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110974_b15","series-title":"Machine Learning for Medical Image Reconstruction: First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings 1","first-page":"12","article-title":"ETER-net: end to end MR image reconstruction using recurrent neural network","author":"Oh","year":"2018"},{"issue":"3","key":"10.1016\/j.bspc.2026.110974_b16","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1109\/JBHI.2023.3348328","article-title":"Multi-contrast complementary learning for accelerated MR imaging","volume":"28","author":"Li","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110974_b17","article-title":"PEARL: Cascaded self-supervised cross-fusion learning for parallel mri acceleration","author":"Zhu","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110974_b18","first-page":"1","article-title":"MAC-net: Model-aided nonlocal neural network for hyperspectral image denoising","volume":"60","author":"Xiong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"10.1016\/j.bspc.2026.110974_b19","doi-asserted-by":"crossref","first-page":"2558","DOI":"10.1109\/TCYB.2021.3127657","article-title":"Autobcs: Block-based image compressive sensing with data-driven acquisition and noniterative reconstruction","volume":"53","author":"Gan","year":"2021","journal-title":"IEEE Trans. Cybern."},{"issue":"10","key":"10.1016\/j.bspc.2026.110974_b20","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.3390\/f15101814","article-title":"Tcsnet: A new individual tree crown segmentation network from unmanned aerial vehicle images","volume":"15","author":"Chi","year":"2024","journal-title":"Forests"},{"key":"10.1016\/j.bspc.2026.110974_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104821","article-title":"An interpretable MRI reconstruction network with two-grid-cycle correction and geometric prior distillation","volume":"84","author":"Fan","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110974_b22","article-title":"Joint edge optimization deep unfolding network for accelerated mri reconstruction","author":"Luo","year":"2024","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"10.1016\/j.bspc.2026.110974_b23","article-title":"Misalignment-resistant deep unfolding network for multi-modal mri super-resolution and reconstruction","volume":"296","year":"2024","journal-title":"Knowl.-Based Syst.","ISSN":"https:\/\/id.crossref.org\/issn\/0950-7051","issn-type":"print"},{"key":"10.1016\/j.bspc.2026.110974_b24","article-title":"Deep maximum a posterior estimator for accelerated MRI reconstruction","volume":"310","year":"2025","journal-title":"Knowl.-Based Syst.","ISSN":"https:\/\/id.crossref.org\/issn\/0950-7051","issn-type":"print"},{"issue":"48","key":"10.1016\/j.bspc.2026.110974_b25","doi-asserted-by":"crossref","first-page":"30088","DOI":"10.1073\/pnas.1907377117","article-title":"On instabilities of deep learning in image reconstruction and the potential costs of AI","volume":"117","author":"Antun","year":"2020","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"3","key":"10.1016\/j.bspc.2026.110974_b26","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1109\/18.382009","article-title":"De-noising by soft-thresholding","volume":"41","author":"Donoho","year":"1995","journal-title":"IEEE Trans. Inform. Theory"},{"issue":"2","key":"10.1016\/j.bspc.2026.110974_b27","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TIP.2011.2165289","article-title":"Kronecker compressive sensing","volume":"21","author":"Duarte","year":"2011","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"10.1016\/j.bspc.2026.110974_b28","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.: An Off. J. Int. Soc. Magn. Reson. Med."},{"issue":"5","key":"10.1016\/j.bspc.2026.110974_b29","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1109\/TMI.2010.2100850","article-title":"Accelerated dynamic MRI exploiting sparsity and low-rank structure: kt SLR","volume":"30","author":"Lingala","year":"2011","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"10.1016\/j.bspc.2026.110974_b30","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1109\/TMI.2010.2090538","article-title":"MR image reconstruction from highly undersampled k-space data by dictionary learning","volume":"30","author":"Ravishankar","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110974_b31","unstructured":"K. Gregor, Y. LeCun, Learning fast approximations of sparse coding, in: Proceedings of the 27th International Conference on International Conference on Machine Learning, 2010, pp. 399\u2013406."},{"key":"10.1016\/j.bspc.2026.110974_b32","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/TCI.2021.3136759","article-title":"Deep unfolding network for spatiospectral image super-resolution","volume":"8","author":"Ma","year":"2021","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"10.1016\/j.bspc.2026.110974_b33","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/TCI.2023.3306100","article-title":"A neural-network-based convex regularizer for inverse problems","volume":"9","author":"Goujon","year":"2023","journal-title":"IEEE Trans. Comput. Imaging"},{"issue":"10","key":"10.1016\/j.bspc.2026.110974_b34","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1109\/TPAMI.2018.2873610","article-title":"Denoising prior driven deep neural network for image restoration","volume":"41","author":"Dong","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.bspc.2026.110974_b35","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/TMI.2018.2865356","article-title":"Modl: Model-based deep learning architecture for inverse problems","volume":"38","author":"Aggarwal","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110974_b36","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part II 23","first-page":"64","article-title":"End-to-end variational networks for accelerated MRI reconstruction","author":"Sriram","year":"2020"},{"key":"10.1016\/j.bspc.2026.110974_b37","doi-asserted-by":"crossref","unstructured":"J. Zhang, B. Ghanem, ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1828\u20131837.","DOI":"10.1109\/CVPR.2018.00196"},{"issue":"3","key":"10.1016\/j.bspc.2026.110974_b38","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1109\/TPAMI.2018.2883941","article-title":"ADMM-csnet: A deep learning approach for image compressive sensing","volume":"42","author":"Yang","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.bspc.2026.110974_b39","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1137\/080716542","article-title":"A fast iterative shrinkage-thresholding algorithm for linear inverse problems","volume":"2","author":"Beck","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"issue":"6","key":"10.1016\/j.bspc.2026.110974_b40","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1002\/mrm.26977","article-title":"Learning a variational network for reconstruction of accelerated mri data","volume":"79","author":"Hammernik","year":"2018","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.bspc.2026.110974_b41","doi-asserted-by":"crossref","unstructured":"J. Song, B. Chen, J. Zhang, Memory-augmented deep unfolding network for compressive sensing, in: Proceedings of the 29th ACM International Conference on Multimedia, 2021, pp. 4249\u20134258.","DOI":"10.1145\/3474085.3475562"},{"key":"10.1016\/j.bspc.2026.110974_b42","article-title":"Multi-contrast MRI reconstruction via information-growth holistic unfolding network","author":"Chen","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.bspc.2026.110974_b43","series-title":"ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"2460","article-title":"FSOINET: Feature-space optimization-inspired network for image compressive sensing","author":"Chen","year":"2022"},{"key":"10.1016\/j.bspc.2026.110974_b44","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part IV 22","first-page":"713","article-title":"VS-net: Variable splitting network for accelerated parallel mri reconstruction","author":"Duan","year":"2019"},{"key":"10.1016\/j.bspc.2026.110974_b45","doi-asserted-by":"crossref","unstructured":"G. Yang, L. Zhang, M. Zhou, A. Liu, X. Chen, Z. Xiong, F. Wu, Model-guided multi-contrast deep unfolding network for MRI super-resolution reconstruction, in: Proceedings of the 30th ACM International Conference on Multimedia, 2022, pp. 3974\u20133982.","DOI":"10.1145\/3503161.3548068"},{"key":"10.1016\/j.bspc.2026.110974_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.129771","article-title":"MCU-net: A multi-prior collaborative deep unfolding network with gates-controlled spatial attention for accelerated MRI reconstruction","volume":"633","author":"Qiao","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.bspc.2026.110974_b47","doi-asserted-by":"crossref","unstructured":"S. Woo, J. Park, J.Y. Lee, I.S. Kweon, Cbam: Convolutional block attention module, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"10.1016\/j.bspc.2026.110974_b48","article-title":"Deep ADMM-net for compressive sensing MRI","volume":"29","author":"Sun","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110974_b49","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.neuroimage.2017.08.021","article-title":"An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement","volume":"170","author":"Souza","year":"2018","journal-title":"NeuroImage"},{"key":"10.1016\/j.bspc.2026.110974_b50","series-title":"A method for stochastic optimization","author":"Adam","year":"2014"},{"key":"10.1016\/j.bspc.2026.110974_b51","doi-asserted-by":"crossref","unstructured":"J. Song, C. Mou, S. Wang, S. Ma, J. Zhang, Optimization-inspired cross-attention transformer for compressive sensing, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 6174\u20136184.","DOI":"10.1109\/CVPR52729.2023.00598"},{"key":"10.1016\/j.bspc.2026.110974_b52","first-page":"1","article-title":"SODAS-net: side-information-aided deep adaptive shrinkage network for compressive sensing","volume":"72","author":"Song","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"6","key":"10.1016\/j.bspc.2026.110974_b53","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1007\/s11263-023-01765-2","article-title":"Deep memory-augmented proximal unrolling network for compressive sensing","volume":"131","author":"Song","year":"2023","journal-title":"Int. J. Comput. Vis."},{"issue":"1","key":"10.1016\/j.bspc.2026.110974_b54","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/TMI.2023.3314747","article-title":"Reconformer: Accelerated mri reconstruction using recurrent transformer","volume":"43","author":"Guo","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110974_b55","doi-asserted-by":"crossref","unstructured":"X. Wang, H. Gan, Ufc-net: Unrolling fixed-point continuous network for deep compressive sensing, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 25149\u201325159.","DOI":"10.1109\/CVPR52733.2024.02376"},{"key":"10.1016\/j.bspc.2026.110974_b56","series-title":"2025 IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"4142","article-title":"MambaRecon: MRI reconstruction with structured state space models","author":"Korkmaz","year":"2025"},{"issue":"4","key":"10.1016\/j.bspc.2026.110974_b57","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":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426015284?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426015284?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T11:34:24Z","timestamp":1785152064000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426015284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":57,"alternative-id":["S1746809426015284"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110974","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":"Edge-prior guided deep unfolding network with multi-scale thresholding for magnetic resonance imaging 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.2026.110974","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":"110974"}}