{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T21:28:15Z","timestamp":1776806895721,"version":"3.51.2"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s13042-023-01921-7","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T10:01:54Z","timestamp":1694167314000},"page":"493-503","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Non-local tensor sparse representation and tensor low rank regularization for dynamic MRI reconstruction"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0884-2776","authenticated-orcid":false,"given":"Minan","family":"Gong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guixu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"issue":"4","key":"1921_CR1","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","volume":"52","author":"DL Donoho","year":"2006","unstructured":"Donoho DL et al (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289\u20131306","journal-title":"IEEE Trans Inf Theory"},{"issue":"2","key":"1921_CR2","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/MSP.2007.914728","volume":"25","author":"JM Pauly","year":"2008","unstructured":"Pauly JM (2008) Compressed sensing MRI. Signal Process Mag IEEE 25(2):72\u201382","journal-title":"Signal Process Mag IEEE"},{"issue":"6","key":"1921_CR3","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1002\/mrm.21391","volume":"58","author":"M Lustig","year":"2007","unstructured":"Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58(6):1182\u20131195","journal-title":"Magn Reson Med"},{"key":"1921_CR4","unstructured":"M. Lustig, J. M. Santos, D. L. Donoho, and J. M. Pauly 2006 kt sparse: High frame rate dynamic mri exploiting spatio-temporal sparsity. In: Proceedings of the 13th annual meeting of ISMRM, Seattle. vol. 2420,"},{"issue":"1","key":"1921_CR5","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1002\/mrm.21757","volume":"61","author":"H Jung","year":"2009","unstructured":"Jung H, Sung K, Nayak KS, Kim EY, Ye JC (2009) k-t focuss: a general compressed sensing framework for high resolution dynamic MRI. Magn Reson Med 61(1):103\u2013116","journal-title":"Magn Reson Med"},{"issue":"5","key":"1921_CR6","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1109\/TMI.2010.2068306","volume":"30","author":"LB Montefusco","year":"2010","unstructured":"Montefusco LB, Lazzaro D, Papi S, Guerrini C (2010) A fast compressed sensing approach to 3d MR image reconstruction. IEEE Trans Med Imaging 30(5):1064\u20131075","journal-title":"IEEE Trans Med Imaging"},{"key":"1921_CR7","unstructured":"Yang J, Zhang Y, Yin W (2008) A fast tvl1-l2 minimization algorithm for signal reconstruction from partial fourier data. Tech Rep. https:\/\/hdl.handle.net\/1911\/102105. Accessed 20 Dec 2017"},{"key":"1921_CR8","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.media.2015.05.012","volume":"27","author":"Z Lai","year":"2016","unstructured":"Lai Z, Qu X, Liu Y, Guo D, Ye J, Zhan Z, Chen Z (2016) Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform. Med Image Anal 27:93\u2013104","journal-title":"Med Image Anal"},{"issue":"5","key":"1921_CR9","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1109\/TMI.2010.2090538","volume":"30","author":"S Ravishankar","year":"2010","unstructured":"Ravishankar S, Bresler Y (2010) MR image reconstruction from highly undersampled k-space data by dictionary learning. IEEE Trans Med Imaging 30(5):1028\u20131041","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"1921_CR10","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1109\/TMI.2014.2301271","volume":"33","author":"J Caballero","year":"2014","unstructured":"Caballero J, Price AN, Rueckert D, Hajnal JV (2014) Dictionary learning and time sparsity for dynamic MR data reconstruction. IEEE Trans Med Imaging 33(4):979\u2013994","journal-title":"IEEE Trans Med Imaging"},{"issue":"6","key":"1921_CR11","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1109\/TMI.2013.2255133","volume":"32","author":"SG Lingala","year":"2013","unstructured":"Lingala SG, Jacob M (2013) Blind compressive sensing dynamic MRI. IEEE Trans Med Imaging 32(6):1132\u20131145","journal-title":"IEEE Trans Med Imaging"},{"issue":"6","key":"1921_CR12","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TMI.2018.2820120","volume":"37","author":"TM Quan","year":"2018","unstructured":"Quan TM, Nguyen-Duc T, Jeong W-K (2018) Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans Med Imaging 37(6):1488\u20131497","journal-title":"IEEE Trans Med Imaging"},{"key":"1921_CR13","doi-asserted-by":"crossref","unstructured":"J. Schlemper, J. Caballero, J. V. Hajnal, A. Price, D. Rueckert (2017) A deep cascade of convolutional neural networks for mr image reconstruction, in Information Processing in Medical Imaging: 25th International Conference, IPMI (2017) Boone, NC, USA, June 25\u201330, 2017, Proceedings 25. Springer. p 647\u2013658","DOI":"10.1007\/978-3-319-59050-9_51"},{"issue":"1","key":"1921_CR14","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1109\/TMI.2018.2863670","volume":"38","author":"C Qin","year":"2018","unstructured":"Qin C, Schlemper J, Caballero J, Price AN, Hajnal JV, Rueckert D (2018) Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging 38(1):280\u2013290","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"1921_CR15","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TMI.2018.2865356","volume":"38","author":"HK Aggarwal","year":"2018","unstructured":"Aggarwal HK, Mani MP, Jacob M (2018) Modl: model-based deep learning architecture for inverse problems. IEEE Trans Med Imaging 38(2):394\u2013405","journal-title":"IEEE Trans Med Imaging"},{"key":"1921_CR16","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.neucom.2022.04.051","volume":"493","author":"J Huang","year":"2022","unstructured":"Huang J, Fang Y, Wu Y, Wu H, Gao Z, Li Y, Del Ser J, Xia J, Yang G (2022) Swin transformer for fast MRI. Neurocomputing 493:281\u2013304","journal-title":"Neurocomputing"},{"issue":"8","key":"1921_CR17","doi-asserted-by":"publisher","first-page":"3618","DOI":"10.1109\/TIP.2014.2329449","volume":"23","author":"W Dong","year":"2014","unstructured":"Dong W, Shi G, Li X, Ma Y, Huang F (2014) Compressive sensing via nonlocal low-rank regularization. IEEE Trans Image Process 23(8):3618\u20133632","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"1921_CR18","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/s10851-016-0647-7","volume":"56","author":"EM Eksioglu","year":"2016","unstructured":"Eksioglu EM (2016) Decoupled algorithm for MRI reconstruction using nonlocal block matching model: Bm3d-MRI. J Math Imaging Vision 56(3):430\u2013440","journal-title":"J Math Imaging Vision"},{"key":"1921_CR19","doi-asserted-by":"crossref","unstructured":"J. Ai, S. Ma, H. Du, and L. Fang (2018) Dynamic MRI reconstruction using tensor-svd. In: 2018 14th IEEE International Conference on Signal Processing (ICSP). IEEE. pp. 1114\u20131118","DOI":"10.1109\/ICSP.2018.8652421"},{"issue":"9","key":"1921_CR20","doi-asserted-by":"publisher","first-page":"2119","DOI":"10.1109\/TMI.2016.2550204","volume":"35","author":"J He","year":"2016","unstructured":"He J, Liu Q, Christodoulou AG, Ma C, Lam F, Liang Z-P (2016) Accelerated high-dimensional MR imaging with sparse sampling using low-rank tensors. IEEE Trans Med Imaging 35(9):2119\u20132129","journal-title":"IEEE Trans Med Imaging"},{"issue":"6","key":"1921_CR21","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0098441","volume":"9","author":"Y Yu","year":"2014","unstructured":"Yu Y, Jin J, Liu F, Crozier S (2014) Multidimensional compressed sensing MRI using tensor decomposition-based sparsifying transform. PLoS One 9(6):e98441","journal-title":"PLoS One"},{"key":"1921_CR22","doi-asserted-by":"crossref","unstructured":"S. F. Roohi, D. Zonoobi, A. A. Kassim, and J. L. Jaremko (2016) Dynamic MRI reconstruction using low rank plus sparse tensor decomposition. In: 2016 IEEE International Conference on Image Processing (ICIP). IEEE. p 1769\u20131773","DOI":"10.1109\/ICIP.2016.7532662"},{"key":"1921_CR23","unstructured":"R. Ramb, M. Zenge, L. Feng, M. Muckley, C. Forman, L. Axel, D. Sodickson, and R. Otazo, Low-rank plus sparse tensor reconstruction for high-dimensional cardiac mri, in Proc. ISMRM, vol. 1199, 2017"},{"issue":"3","key":"1921_CR24","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1002\/mrm.25240","volume":"73","author":"R Otazo","year":"2015","unstructured":"Otazo R, Candes E, Sodickson DK (2015) Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magn Reson Med 73(3):1125\u20131136","journal-title":"Magn Reson Med"},{"key":"1921_CR25","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.mri.2016.10.025","volume":"37","author":"X Yang","year":"2017","unstructured":"Yang X, Luo Y, Chen S, Zhen X, Yu Q, Liu K (2017) Dynamic MRI reconstruction from highly undersampled (k, t)-space data using weighted schatten p-norm regularizer of tensor. Magn Reson Imaging 37:260\u2013272","journal-title":"Magn Reson Imaging"},{"key":"1921_CR26","doi-asserted-by":"crossref","unstructured":"S. Wu, Y. Liu, T. Liu, F. Wen, S. Liang, X. Zhang, S. Wang, and C. Zhu (2018) Multiple low-ranks plus sparsity based tensor reconstruction for dynamic MRI. In: 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP). IEEE. p 1\u20135","DOI":"10.1109\/ICDSP.2018.8631646"},{"issue":"4","key":"1921_CR27","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1002\/mrm.22883","volume":"66","author":"M Usman","year":"2011","unstructured":"Usman M, Prieto C, Schaeffter T, Batchelor P (2011) k-t group sparse: a method for accelerating dynamic MRI. Magn Reson Med 66(4):1163\u20131176","journal-title":"Magn Reson Med"},{"issue":"3","key":"1921_CR28","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1109\/TIP.2010.2076294","volume":"20","author":"MV Afonso","year":"2010","unstructured":"Afonso MV, Bioucas-Dias JM, Figueiredo MA (2010) An augmented lagrangian approach to the constrained optimization formulation of imaging inverse problems. IEEE Trans Image Process 20(3):681\u2013695","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"1921_CR29","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1137\/07070111X","volume":"51","author":"TG Kolda","year":"2009","unstructured":"Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455\u2013500","journal-title":"SIAM Rev"},{"key":"1921_CR30","doi-asserted-by":"crossref","unstructured":"N. Qi, Y. Shi, X. Sun, and B. Yin (2016) Tensr: Multi-dimensional tensor sparse representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. p 5916\u20135925","DOI":"10.1109\/CVPR.2016.637"},{"issue":"1","key":"1921_CR31","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1162\/NECO_a_00385","volume":"25","author":"CF Caiafa","year":"2013","unstructured":"Caiafa CF, Cichocki A (2013) Computing sparse representations of multidimensional signals using kronecker bases. Neural Comput 25(1):186\u2013220","journal-title":"Neural Comput"},{"key":"1921_CR32","unstructured":"Zhang Z, Aeron S (2015) Denoising and completion of 3d data via multidimensional dictionary learning. arXiv:1512.09227. Accessed 12 Oct 2018"},{"issue":"1","key":"1921_CR33","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1109\/TPAMI.2012.39","volume":"35","author":"J Liu","year":"2012","unstructured":"Liu J, Musialski P, Wonka P, Ye J (2012) Tensor completion for estimating missing values in visual data. IEEE Trans Pattern Anal Mach Intell 35(1):208\u2013220","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1921_CR34","doi-asserted-by":"crossref","unstructured":"Z. Zhang, G. Ely, S. Aeron, N. Hao, and M. Kilmer (2014) Novel methods for multilinear data completion and de-noising based on tensor-svd. In: Proceedings of the IEEE conference on computer vision and pattern recognition. p 3842\u20133849","DOI":"10.1109\/CVPR.2014.485"},{"issue":"3","key":"1921_CR35","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1016\/j.laa.2010.09.020","volume":"435","author":"ME Kilmer","year":"2011","unstructured":"Kilmer ME, Martin CD (2011) Factorization strategies for third-order tensors. Linear Algebra Appl 435(3):641\u2013658","journal-title":"Linear Algebra Appl"},{"issue":"4","key":"1921_CR36","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.1109\/TIP.2014.2305840","volume":"23","author":"O Semerci","year":"2014","unstructured":"Semerci O, Hao N, Kilmer ME, Miller EL (2014) Tensor-based formulation and nuclear norm regularization for multienergy computed tomography. IEEE Trans Image Process 23(4):1678\u20131693","journal-title":"IEEE Trans Image Process"},{"key":"1921_CR37","unstructured":"A. Buades, B. Coll, and J.-M. Morel (2005) A non-local algorithm for image denoising, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), vol. 2. IEEE. p 60\u201365"},{"issue":"1","key":"1921_CR38","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1109\/TPAMI.2017.2663423","volume":"40","author":"N Qi","year":"2017","unstructured":"Qi N, Shi Y, Sun X, Wang J, Yin B, Gao J (2017) Multi-dimensional sparse models. IEEE Trans Pattern Anal Mach Intell 40(1):163\u2013178","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"13","key":"1921_CR39","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1049\/el:20080522","volume":"44","author":"Q Huynh-Thu","year":"2008","unstructured":"Huynh-Thu Q, Ghanbari M (2008) Scope of validity of psnr in image\/video quality assessment. Electron Lett 44(13):800\u2013801","journal-title":"Electron Lett"},{"issue":"4","key":"1921_CR40","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"1921_CR41","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.media.2017.11.003","volume":"44","author":"J Yao","year":"2018","unstructured":"Yao J, Xu Z, Huang X, Huang J (2018) An efficient algorithm for dynamic MRI using low-rank and total variation regularizations. Med Image Anal 44:14\u201327","journal-title":"Med Image Anal"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01921-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01921-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01921-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T16:29:29Z","timestamp":1705076969000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01921-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,8]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1921"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01921-7","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,8]]},"assertion":[{"value":"18 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}