{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:27:07Z","timestamp":1768566427807,"version":"3.49.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,5,5]],"date-time":"2020-05-05T00:00:00Z","timestamp":1588636800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,5]],"date-time":"2020-05-05T00:00:00Z","timestamp":1588636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672466"],"award-info":[{"award-number":["61672466"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671405"],"award-info":[{"award-number":["61671405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LSZ19F010001"],"award-info":[{"award-number":["LSZ19F010001"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LY18D060009"],"award-info":[{"award-number":["LY18D060009"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1007\/s11517-020-02161-5","type":"journal-article","created":{"date-parts":[[2020,5,5]],"date-time":"2020-05-05T18:02:53Z","timestamp":1588701773000},"page":"1483-1498","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improved robust tensor principal component analysis for accelerating dynamic MR imaging reconstruction"],"prefix":"10.1007","volume":"58","author":[{"given":"Mingfeng","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Qiannan","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiaocheng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jucheng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yaming","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ling","family":"Xia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,5,5]]},"reference":[{"key":"2161_CR1","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1080\/10382040008667647","volume":"9","author":"K Oost","year":"2000","unstructured":"Oost K, Kanneworff AG (2000) Compressed sensing MRI. Int Res Geogr Environ Educ 9:176\u2013180","journal-title":"Int Res Geogr Environ Educ"},{"issue":"6","key":"2161_CR2","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.2214\/AJR.12.8510","volume":"198","author":"S Verma","year":"2012","unstructured":"Verma S, Turkbey B, Muradyan N, Rajesh A, Cornud F, Haider MA, Choyke PL, Harisinghani M (2012) Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management. Am J Roentgenol 198(6):1277\u20131288","journal-title":"Am J Roentgenol"},{"key":"2161_CR3","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1002\/mrm.24980","volume":"72","author":"L Feng","year":"2014","unstructured":"Feng L, Grimm R, Block KT et al (2014) Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med 72:707\u2013717. https:\/\/doi.org\/10.1002\/mrm.24980","journal-title":"Magn Reson Med"},{"key":"2161_CR4","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1007\/s11517-017-1763-2","volume":"56","author":"J Luo","year":"2018","unstructured":"Luo J, Mou Z, Qin B et al (2018) A singular K-space model for fast reconstruction of magnetic resonance images from undersampled data. Med Biol Eng Comput 56:1211\u20131225. https:\/\/doi.org\/10.1007\/s11517-017-1763-2","journal-title":"Med Biol Eng Comput"},{"key":"2161_CR5","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1002\/mrm.21477","volume":"59","author":"U Gamper","year":"2008","unstructured":"Gamper U, Boesiger P, Kozerke S (2008) Compressed sensing in dynamic MRI. Magn Reson Med 59:365\u2013373. https:\/\/doi.org\/10.1002\/mrm.21477","journal-title":"Magn Reson Med"},{"key":"2161_CR6","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1002\/mrm.22463","volume":"64","author":"R Otazo","year":"2010","unstructured":"Otazo R, Kim D, Axel L, Sodickson DK (2010) Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. Magn Reson Med 64:767\u2013776. https:\/\/doi.org\/10.1002\/mrm.22463","journal-title":"Magn Reson Med"},{"key":"2161_CR7","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1016\/j.jestch.2017.07.001","volume":"20","author":"M Sandilya","year":"2017","unstructured":"Sandilya M, Nirmala SR (2017) Compressed sensing trends in magnetic resonance imaging. Eng Sci Technol an Int J 20:1342\u20131352. https:\/\/doi.org\/10.1016\/j.jestch.2017.07.001","journal-title":"Eng Sci Technol an Int J"},{"key":"2161_CR8","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 58:1182\u20131195. https:\/\/doi.org\/10.1002\/mrm.21391","journal-title":"Magn Reson Med"},{"key":"2161_CR9","doi-asserted-by":"crossref","unstructured":"Zhong W, Li D, Wang L, Zhang M (2016) Low-rank plus sparse reconstruction using dictionary learning for 3D-MRI. In: 9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI) pp:1407\u20131411","DOI":"10.1109\/CISP-BMEI.2016.7852937"},{"key":"2161_CR10","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1109\/TPAMI.2017.2734888","volume":"40","author":"Q Xie","year":"2017","unstructured":"Xie Q, Zhao Q, Meng D, Xu Z (2017) Kronecker-basis-representation based tensor sparsity and its applications to tensor recovery. IEEE trans on pattern analysis and machine intelligence 40:1888\u20131902. https:\/\/doi.org\/10.1109\/TPAMI.2017.2734888","journal-title":"IEEE trans on pattern analysis and machine intelligence"},{"key":"2161_CR11","first-page":"306","volume-title":"IEEE 13th international symposium on biomedical imaging(ISBI)","author":"SC Ula","year":"2016","unstructured":"Ula SC, Pedro AG et al (2016) Spatio-temporal MRI reconstruction by enforcing local and global regularity via dynamic total and nuclear norm minimization. In: IEEE 13th international symposium on biomedical imaging(ISBI), pp 306\u2013309"},{"key":"2161_CR12","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.ins.2018.03.064","volume":"451\u2013452","author":"S Liu","year":"2018","unstructured":"Liu S, Cao J, Liu H et al (2018) Group sparsity with orthogonal dictionary and nonconvex regularization for exact MRI reconstruction. Inf Sci 451\u2013452:161\u2013179. https:\/\/doi.org\/10.1016\/j.ins.2018.03.064","journal-title":"Inf Sci"},{"issue":"11","key":"2161_CR13","doi-asserted-by":"publisher","first-page":"3083","DOI":"10.1109\/TBME.2013.2266096","volume":"60","author":"AG Christodoulou","year":"2013","unstructured":"Christodoulou AG, Zhang H, Zhao B, Hitchens TK, Ho C, Liang ZP (2013) High-resolution cardiovascular MRI by integrating parallel imaging with low-rank and sparse modeling. IEEE Trans Biomed Eng 60(11):3083\u20133092","journal-title":"IEEE Trans Biomed Eng"},{"key":"2161_CR14","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1109\/ACCESS.2017.2657645","volume":"5","author":"F Xu","year":"2017","unstructured":"Xu F, Han J, Wang Y et al (2017) Dynamic magnetic resonance imaging via nonconvex low-rank matrix approximation. IEEE Access 5:1958\u20131966. https:\/\/doi.org\/10.1109\/ACCESS.2017.2657645","journal-title":"IEEE Access"},{"key":"2161_CR15","doi-asserted-by":"publisher","first-page":"7309","DOI":"10.1088\/0031-9155\/58\/20\/7309","volume":"58","author":"SG Lingala","year":"2013","unstructured":"Lingala SG, Dibella E, Adluru G (2013) Accelerating free breathing myocardial perfusion MRI using multi-coil radial k \u2212 t SLR. Phys Med Biol 58:7309\u20137327. https:\/\/doi.org\/10.1088\/0031-9155\/58\/20\/7309","journal-title":"Phys Med Biol"},{"issue":"8","key":"2161_CR16","doi-asserted-by":"publisher","first-page":"1689","DOI":"10.1109\/TMI.2014.2321190","volume":"33","author":"B Tr\u00e9moulh\u00e9ac","year":"2014","unstructured":"Tr\u00e9moulh\u00e9ac B, Dikaios N, Atkinson D et al (2014) Dynamic MR image reconstruction-separation from undersampled (k, t)-space via low-rank plus sparse prior. IEEE transactions on medical imaging 33(8):1689\u20131701","journal-title":"IEEE transactions on medical imaging"},{"key":"2161_CR17","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1002\/mrm.25240","volume":"73","author":"R Otazo","year":"2015","unstructured":"Otazo R, Sodickson DK, Cande E (2015) Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magn Reson Med 73:1125\u20131136. https:\/\/doi.org\/10.1002\/mrm.25240","journal-title":"Magn Reson Med"},{"issue":"9","key":"2161_CR18","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.1109\/TMI.2012.2203921","volume":"31","author":"B Zhao","year":"2012","unstructured":"Zhao B, Haldar JP, Christodoulou AG et al (2012) Image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints. IEEE transactions on medical imaging 31(9):1809\u20131820","journal-title":"IEEE transactions on medical imaging"},{"key":"2161_CR19","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 32:1132\u20131145","journal-title":"IEEE Trans Med Imaging"},{"key":"2161_CR20","doi-asserted-by":"publisher","first-page":"2004","DOI":"10.1109\/TSP.2017.2649482","volume":"65","author":"M Rahmani","year":"2017","unstructured":"Rahmani M, Atia GK (2017) High dimensional low rank plus sparse matrix decomposition. IEEE Trans Signal Process 65:2004\u20132019","journal-title":"IEEE Trans Signal Process"},{"key":"2161_CR21","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 51:455\u2013500","journal-title":"SIAM Rev"},{"key":"2161_CR22","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.laa.2017.02.028","volume":"523","author":"H Rauhut","year":"2017","unstructured":"Rauhut H, Schneider R, Stojanac \u017d (2017) Low rank tensor recovery via iterative hard thresholding. Linear Algebra Appl 523:220\u2013262. https:\/\/doi.org\/10.1016\/j.laa.2017.02.028","journal-title":"Linear Algebra Appl"},{"key":"2161_CR23","doi-asserted-by":"publisher","first-page":"3551","DOI":"10.1145\/242224.242229","volume":"65","author":"ND Sidiropoulos","year":"2017","unstructured":"Sidiropoulos ND, Lathauwer D et al (2017) Tensor decomposition for signal processing and machine learning. IEEE Trans Signal Process 65:3551\u20133582. https:\/\/doi.org\/10.1145\/242224.242229","journal-title":"IEEE Trans Signal Process"},{"issue":"14","key":"2161_CR24","doi-asserted-by":"publisher","first-page":"3702","DOI":"10.1109\/TSP.2017.2695566","volume":"65","author":"J Ying","year":"2017","unstructured":"Ying J, Lu H, Wei Q et al (2017) Hankel matrix nuclear norm regularized tensor completion for N-dimensional exponential signals. IEEE Trans Signal Process 65(14):3702\u20133717","journal-title":"IEEE Trans Signal Process"},{"key":"2161_CR25","doi-asserted-by":"publisher","first-page":"4731","DOI":"10.1109\/TGRS.2018.2835514","volume":"56","author":"J An","year":"2018","unstructured":"An J, Zhang X, Zhou H, Jiao L (2018) Tensor-based low-rank graph with multimanifold regularization for dimensionality reduction of hyperspectral images. IEEE Trans Geosci Remote Sens 56:4731\u20134746. https:\/\/doi.org\/10.1109\/TGRS.2018.2835514","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2161_CR26","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1007\/s10208-015-9264-x","volume":"16","author":"H Derksen","year":"2016","unstructured":"Derksen H (2016) On the nuclear norm and the singular value decomposition of tensor. Found Comput Math 16:779\u2013811. https:\/\/doi.org\/10.1007\/s10208-015-9264-x","journal-title":"Found Comput Math"},{"key":"2161_CR27","doi-asserted-by":"publisher","unstructured":"Liu Y, Member S, Long Z, Zhu C (2018) Image completion using low tensor tree rank and total variation minimization. IEEE Trans Multimed:1. https:\/\/doi.org\/10.1109\/TMM.2018.2859026","DOI":"10.1109\/TMM.2018.2859026"},{"key":"2161_CR28","doi-asserted-by":"publisher","first-page":"2919","DOI":"10.1109\/TGRS.2017.2786718","volume":"56","author":"Y Xu","year":"2018","unstructured":"Xu Y, Wu Z et al (2018) Joint reconstruction and anomaly detection from compressive hyperspectral images using Mahalanobis distance-regularized tensor RPCA. IEEE Trans Geosci Remote Sens 56:2919\u20132930","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2161_CR29","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1109\/TIP.2017.2762595","volume":"27","author":"P Zhou","year":"2018","unstructured":"Zhou P, Lu C, Member S et al (2018) Tensor factorization for low-rank tensor completion. IEEE Trans Image Process 27:1152\u20131163","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"2161_CR30","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 ZP (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"},{"key":"2161_CR31","doi-asserted-by":"publisher","first-page":"1933","DOI":"10.1007\/s11517-019-02005-x13","volume":"57","author":"J Huang","year":"2019","unstructured":"Huang J, Zhou G, Yu G (2019) Orthogonal tensor dictionary learning for accelerated dynamic MRI. Med Biol Eng Comput 57:1933\u20131946. https:\/\/doi.org\/10.1007\/s11517-019-02005-x13","journal-title":"Med Biol Eng Comput"},{"key":"2161_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal","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 9:1\u201313. https:\/\/doi.org\/10.1371\/journal","journal-title":"PLoS One"},{"key":"2161_CR33","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1016\/j.patcog.30.31.32","volume":"63","author":"SF Roohi","year":"2017","unstructured":"Roohi SF, Zonoobi D, Kassim AA, Jaremko JL (2017) Multi-dimensional low rank plus sparse decomposition for reconstruction of under-sampled dynamic MRI. Pattern Recogn 63:667\u2013679. https:\/\/doi.org\/10.1016\/j.patcog.30.31.32","journal-title":"Pattern Recogn"},{"key":"2161_CR34","doi-asserted-by":"publisher","unstructured":"Lu C, Feng J, Chen Y et al (2016) Tensor robust principal component analysis: exact recovery of corrupted low-rank tensors via convex optimization. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit:5249\u20135257. https:\/\/doi.org\/10.1109\/CVPR.2016.567","DOI":"10.1109\/CVPR.2016.567"},{"key":"2161_CR35","first-page":"1","volume-title":"2018 IEEE Int Conf Multimed Expo","author":"L Chen","year":"2018","unstructured":"Chen L, Liu Y, Zhu C (2018) Robust tensor principal component analysis in all modes. In: 2018 IEEE Int Conf Multimed Expo, pp 1\u20136"},{"key":"2161_CR36","doi-asserted-by":"publisher","unstructured":"Liu Y, Member S, Chen L, Zhu C (2018) Improved robust tensor principle component analysis via low rank core matrix. IEEE J Sel Top Signal Process:1. https:\/\/doi.org\/10.1109\/JSTSP.2018.2873142","DOI":"10.1109\/JSTSP.2018.2873142"},{"key":"2161_CR37","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s00521-015-2050-5","volume":"29","author":"C Zhang","year":"2018","unstructured":"Zhang C, Hu W, Jin T, Mei Z (2018) Nonlocal image denoising via adaptive tensor nuclear norm minimization. Neural Comput Applic 29:3\u201319. https:\/\/doi.org\/10.1007\/s00521-015-2050-5","journal-title":"Neural Comput Applic"},{"key":"2161_CR38","doi-asserted-by":"crossref","unstructured":"Lu C, Feng J, Chen Y et al (2019) Tensor robust principle component analysis with a new tensor nuclear norm. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2019.2891760"},{"key":"2161_CR39","doi-asserted-by":"publisher","first-page":"4075","DOI":"10.1109\/TIP.2016.2579262","volume":"25","author":"W Cao","year":"2016","unstructured":"Cao W, Wang Y, Sun J, Meng D, Yang C, Cichocki A, Xu Z (2016) Total variation regularized tensor RPCA for background subtraction from compressive measurements. IEEE Trans Image Process 25:4075\u20134090. https:\/\/doi.org\/10.1109\/TIP.2016.2579262","journal-title":"IEEE Trans Image Process"},{"key":"2161_CR40","doi-asserted-by":"publisher","first-page":"074001","DOI":"10.1088\/1361-6420\/aac3af","volume":"34","author":"J Rasch","year":"2018","unstructured":"Rasch J, Kolehmainen V, Nivajarvi R et al (2018) Dynamic MRI reconstruction from undersampled data with an anatomical prescan. Inverse Probl 34:074001. https:\/\/doi.org\/10.1088\/1361-6420\/aac3af","journal-title":"Inverse Probl"},{"key":"2161_CR41","doi-asserted-by":"publisher","unstructured":"Wang S, Ke Z, Cheng H et al (2018) DIMENSION: dynamic MR imaging with both k-space and spatial prior knowledge obtained via multi-supervised network training. NMR Biomed. https:\/\/doi.org\/10.1002\/nbm.4131","DOI":"10.1002\/nbm.4131"},{"key":"2161_CR42","doi-asserted-by":"crossref","unstructured":"Wang, S, Chen, Y, Xiao, T, Ke, Z, Liu, Q, & Zheng, H (2019) LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset. arXiv preprint arXiv:1908.09140","DOI":"10.3934\/ipi.2020051"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-020-02161-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-020-02161-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-020-02161-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T23:12:36Z","timestamp":1620169956000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-020-02161-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,5]]},"references-count":42,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["2161"],"URL":"https:\/\/doi.org\/10.1007\/s11517-020-02161-5","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,5]]},"assertion":[{"value":"22 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}