{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T05:33:43Z","timestamp":1770788023277,"version":"3.50.0"},"reference-count":65,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1016\/j.media.2020.101945","type":"journal-article","created":{"date-parts":[[2020,12,19]],"date-time":"2020-12-19T11:58:53Z","timestamp":1608379133000},"page":"101945","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":21,"special_numbering":"C","title":["Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks"],"prefix":"10.1016","volume":"69","author":[{"given":"Mohammad","family":"Golbabaee","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8386-639X","authenticated-orcid":false,"given":"Guido","family":"Buonincontri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5759-5290","authenticated-orcid":false,"given":"Carolin M.","family":"Pirkl","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0087-9134","authenticated-orcid":false,"given":"Marion I.","family":"Menzel","sequence":"additional","affiliation":[]},{"given":"Bjoern H.","family":"Menze","sequence":"additional","affiliation":[]},{"given":"Mike","family":"Davies","sequence":"additional","affiliation":[]},{"given":"Pedro A.","family":"G\u00f3mez","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.media.2020.101945_bib0001","unstructured":"Arberet, S., Chen, X., Mailhe, B., Nadar, M., Speier, P., 2019. Low rank and spatial regularization model for magnetic resonance fingerprinting. US Patent 2019\/0041480."},{"key":"10.1016\/j.media.2020.101945_bib0002","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1002\/mrm.26639","article-title":"Low rank alternating direction method of multipliers reconstruction for MR fingerprinting","volume":"79","author":"Assl\u00e4nder","year":"2018","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2020.101945_bib0003","series-title":"Proceedings of the Intl. Conference on Machine Learning","first-page":"374","article-title":"A spline theory of deep learning","author":"Balestriero","year":"2018"},{"key":"10.1016\/j.media.2020.101945_bib0004","doi-asserted-by":"crossref","unstructured":"Balsiger, F., Jungo, A., Scheidegger, O., Carlier, P. G., Reyes, M., Marty, B., 2019. Spatially regularized parametric map reconstruction for fast magnetic resonance fingerprinting. arXiv:1911.03786.","DOI":"10.1016\/j.media.2020.101741"},{"key":"10.1016\/j.media.2020.101945_bib0005","series-title":"International Workshop on Machine Learning for Medical Image Reconstruction","first-page":"39","article-title":"Magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks","author":"Balsiger","year":"2018"},{"key":"10.1016\/j.media.2020.101945_bib0006","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."},{"key":"10.1016\/j.media.2020.101945_bib0007","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.mri.2019.04.014","article-title":"Multi-shot echo planar imaging for accelerated cartesian MR fingerprinting: an alternative to conventional spiral MR fingerprinting","volume":"61","author":"Benjamin","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.media.2020.101945_bib0008","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1002\/mrm.26009","article-title":"MR fingerprinting with simultaneous B1 estimation","volume":"76","author":"Buonincontri","year":"2016","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2020.101945_bib0009","doi-asserted-by":"crossref","first-page":"3705","DOI":"10.1002\/mrm.27694","article-title":"High-dimensionality undersampled patch-based reconstruction (HD-PROST) for accelerated multi-contrast MRI","volume":"81","author":"Bustin","year":"2019","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2020.101945_bib0010","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1002\/mrm.25439","article-title":"Fast group matching for MR fingerprinting reconstruction","volume":"74","author":"Cauley","year":"2015","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2020.101945_bib0011","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s10851-010-0251-1","article-title":"A first-order primal-dual algorithm for convex problems with applications to imaging","volume":"40","author":"Chambolle","year":"2011","journal-title":"J. Math. Imaging Vis."},{"key":"10.1016\/j.media.2020.101945_bib0012","doi-asserted-by":"crossref","first-page":"E1181","DOI":"10.1073\/pnas.1302293110","article-title":"Computational and statistical tradeoffs via convex relaxation","volume":"110","author":"Chandrasekaran","year":"2013","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.media.2020.101945_bib0013","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.mri.2017.07.007","article-title":"AIR-MRF: accelerated iterative reconstruction for magnetic resonance fingerprinting","volume":"41","author":"Cline","year":"2017","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.media.2020.101945_bib0014","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1002\/mrm.27198","article-title":"MR fingerprinting deep reconstruction network (DRONE)","volume":"80","author":"Cohen","year":"2018","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2020.101945_bib0015","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1002\/mrm.27448","article-title":"Rigid motion-corrected magnetic resonance fingerprinting","volume":"81","author":"Cruz","year":"2019","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2020.101945_bib0016","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02551274","article-title":"Approximation by superpositions of a sigmoidal function","volume":"2","author":"Cybenko","year":"1989","journal-title":"Math. Control Signals Syst."},{"key":"10.1016\/j.media.2020.101945_bib0017","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1137\/130947246","article-title":"A compressed sensing framework for magnetic resonance fingerprinting","volume":"7","author":"Davies","year":"2014","journal-title":"SIAM J. Imaging Sci."},{"key":"10.1016\/j.media.2020.101945_bib0018","series-title":"Advances in Neural Information Processing Systems","first-page":"666","article-title":"Shallow vs. deep sum-product networks","author":"Delalleau","year":"2011"},{"key":"10.1016\/j.media.2020.101945_bib0019","unstructured":"Duarte, R., Repetti, A., G\u00f3mez, P. A., Davies, M., Wiaux, Y., 2018. Greedy approximate projection for magnetic resonance fingerprinting with partial volumes. arXiv:1807.06912."},{"key":"10.1016\/j.media.2020.101945_bib0022","unstructured":"Fujita, S., Buonincontri, G., Cencini, M., et\u00a0al.,. Repeatability and reproducibility of human brain morphometry using three-dimensional magnetic resonance fingerprinting. Technical Report, Wiley Online Library."},{"issue":"10","key":"10.1016\/j.media.2020.101945_bib0020","doi-asserted-by":"crossref","first-page":"2364","DOI":"10.1109\/TMI.2019.2899328","article-title":"Deep learning for fast and spatially-constrained tissue quantification from highly-accelerated data in magnetic resonance fingerprinting","volume":"38","author":"Fang","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2020.101945_bib0021","series-title":"Medical Image Computing and Computer-Assisted Intervention (MICCAI)","first-page":"101","article-title":"RCA-U-Net: residual channel attention U-Net for fast tissue quantification in magnetic resonance fingerprinting","volume":"11766","author":"Fang","year":"2019"},{"key":"10.1016\/j.media.2020.101945_bib0023","series-title":"ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"7825","article-title":"Geometry of deep learning for magnetic resonance fingerprinting","author":"Golbabaee","year":"2019"},{"key":"10.1016\/j.media.2020.101945_bib0024","series-title":"IEEE Intl. Workshop on Machine Learning for Signal Processing","article-title":"Cover tree compressed sensing for fast MR fingerprint recovery","author":"Golbabaee","year":"2017"},{"key":"10.1016\/j.media.2020.101945_bib0025","doi-asserted-by":"crossref","first-page":"015003","DOI":"10.1088\/1361-6420\/ab4c9a","article-title":"CoverBLIP: accelerated and scalable iterative matched-filtering for magnetic resonance fingerprint reconstruction","volume":"36","author":"Golbabaee","year":"2019","journal-title":"Inverse Probl."},{"key":"10.1016\/j.media.2020.101945_bib0026","series-title":"Proceedings of Intl. Soc. Mag. Res. Med. (ISMRM)","article-title":"Deep MR fingerprinting with total-variation and low-rank subspace priors","author":"Golbabaee","year":"2019"},{"key":"10.1016\/j.media.2020.101945_bib0027","doi-asserted-by":"crossref","unstructured":"Golbabaee, M., Vandergheynst, P., 2012a. Hyperspectral image compressed sensing via low-rank and joint-sparse matrix recovery. IEEE. Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, 2741\u20132744,","DOI":"10.1109\/ICASSP.2012.6288484"},{"key":"10.1016\/j.media.2020.101945_bib0028","doi-asserted-by":"crossref","unstructured":"Golbabaee, M., Vandergheynst, P., 2012b. Joint trace\/tv norm minimization: A new efficient approach for spectral compressive imaging. IEEE. Image Processing (ICIP), 2012 19th IEEE International Conference on, 933\u2013936","DOI":"10.1109\/ICIP.2012.6467014"},{"key":"10.1016\/j.media.2020.101945_bib0029","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-70789-2","article-title":"Rapid three-dimensional multiparametric MRI with quantitative transient-state imaging","volume":"10","author":"G\u00f3mez","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.media.2020.101945_bib0030","series-title":"MICCAI","article-title":"Simultaneous parameter mapping, modality synthesis, and anatomical labeling of the brain with MR fingerprinting","author":"G\u00f3mez","year":"2016"},{"key":"10.1016\/j.media.2020.101945_bib0031","series-title":"1st International Workshop on Patch-based Techniques in Medical Imaging. MICCAI","article-title":"Learning a spatiotemporal dictionary for magnetic resonance fingerprinting with compressed sensing","author":"G\u00f3mez","year":"2015"},{"key":"10.1016\/j.media.2020.101945_bib0032","first-page":"202","article-title":"Deep learning for magnetic resonance fingerprinting: a new approach for predicting quantitative parameter values from time series","volume":"243","author":"Hoppe","year":"2017","journal-title":"Stud. Health Technol. Inform."},{"key":"10.1016\/j.media.2020.101945_bib0033","series-title":"Medical Image Computing and Computer Assisted Intervention (MICCAI)","article-title":"Rinq fingerprinting: recurrence-informed quantile networks for magnetic resonance fingerprinting","author":"Hoppe","year":"2019"},{"key":"10.1016\/j.media.2020.101945_bib0034","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.mri.2020.02.005","article-title":"Free-running cardiac magnetic resonance fingerprinting: joint T1\/T2 map and cine imaging","volume":"68","author":"Jaubert","year":"2020","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.media.2020.101945_bib0035","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1103\/PhysRev.98.1099","article-title":"Matrix treatment of nuclear induction","volume":"98","author":"Jaynes","year":"1955","journal-title":"Phys. Rev."},{"key":"10.1016\/j.media.2020.101945_bib0036","series-title":"Proc. Intl. Soc. Mag. Res. Med.","article-title":"Simultaneous T1, T2 and diffusion quantification using multiple contrast prepared magnetic resonance fingerprinting","author":"Jiang","year":"2017"},{"key":"10.1016\/j.media.2020.101945_bib0037","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1002\/mrm.25559","article-title":"MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout","volume":"74","author":"Jiang","year":"2015","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2020.101945_bib0038","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/0730-725X(93)90199-N","article-title":"Ii. Performance assessment and quality control in MRI by Eurospin test objects and protocols","volume":"11","author":"Lerski","year":"1993","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.media.2020.101945_bib0039","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."},{"key":"10.1016\/j.media.2020.101945_bib0040","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1038\/nature11971","article-title":"Magnetic resonance fingerprinting","volume":"495","author":"Ma","year":"2013","journal-title":"Nature"},{"key":"10.1016\/j.media.2020.101945_bib0041","first-page":"6","article-title":"New technology allows multiple image contrasts in a single scan","volume":"SPRING","author":"Marcel","year":"2015","journal-title":"GESIGNAPULSE.COM\/MR"},{"key":"10.1016\/j.media.2020.101945_bib0042","doi-asserted-by":"crossref","first-page":"4066","DOI":"10.1002\/mp.13078","article-title":"Low-rank magnetic resonance fingerprinting","volume":"45","author":"Mazor","year":"2018","journal-title":"Med. Phys."},{"key":"10.1016\/j.media.2020.101945_bib0043","doi-asserted-by":"crossref","first-page":"2311","DOI":"10.1109\/TMI.2014.2337321","article-title":"SVD compression for magnetic resonance fingerprinting in the time domain","volume":"33","author":"McGivney","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2020.101945_bib0044","series-title":"Advances in Neural Information Processing Systems","first-page":"2924","article-title":"On the number of linear regions of deep neural networks","author":"Montufar","year":"2014"},{"key":"10.1016\/j.media.2020.101945_bib0045","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1109\/TMI.2018.2817547","article-title":"Dictionary-free MRI perk: parameter estimation via regression with kernels","volume":"37","author":"Nataraj","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2020.101945_bib0046","series-title":"Dokl. Akad. Nauk Sssr","first-page":"543","article-title":"A method for solving the convex programming problem with convergence rate o (1\/k\u2227 2)","author":"Nesterov","year":"1983"},{"key":"10.1016\/j.media.2020.101945_bib0047","series-title":"IEEE Intl. Symposium on Biomedical Imaging (ISBI)","first-page":"1537","article-title":"Magnetic resonance fingerprinting using recurrent neural networks","author":"Oksuz","year":"2019"},{"key":"10.1016\/j.media.2020.101945_bib0048","first-page":"558","article-title":"Accelerated 3D multiparametric MRI in glioma patients: initial clinical experience","volume":"8","author":"Pirkl","year":"2020","journal-title":"Int Soc. Mag. Res. Med. (ISMRM)"},{"key":"10.1016\/j.media.2020.101945_bib0049","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1002\/mrm.26561","article-title":"Magnetic resonance fingerprinting using echo-planar imaging: joint quantification of T1 and T2* relaxation times","volume":"78","author":"Rieger","year":"2017","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2020.101945_bib0050","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear total variation based noise removal algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Phys. D"},{"key":"10.1016\/j.media.2020.101945_bib0051","unstructured":"Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556."},{"key":"10.1016\/j.media.2020.101945_bib0052","doi-asserted-by":"crossref","first-page":"4951","DOI":"10.1002\/mp.13727","article-title":"Hydra: hybrid deep magnetic resonance fingerprinting","volume":"46","author":"Song","year":"2019","journal-title":"Med. Phys."},{"key":"10.1016\/j.media.2020.101945_bib0053","doi-asserted-by":"crossref","unstructured":"Tang, J., Egiazarian, K., Golbabaee, M., Davies, M., 2019. The practicality of stochastic optimization in imaging inverse problems. arXiv:1910.10100.","DOI":"10.1109\/TCI.2020.3032101"},{"key":"10.1016\/j.media.2020.101945_bib0054","series-title":"Advances in Neural Information Processing Systems","first-page":"429","article-title":"Rest-Katyusha: exploiting the solution\u2019s structure via scheduled restart schemes","author":"Tang","year":"2018"},{"key":"10.1016\/j.media.2020.101945_bib0055","first-page":"3377","article-title":"Gradient projection iterative sketch for large scale constrained least-squares","volume":"70","author":"Tang","year":"2017","journal-title":"Proc. Int. Conf. Mach. Learn."},{"key":"10.1016\/j.media.2020.101945_bib0056","series-title":"Quantitative MRI of the Brain: Measuring Changes Caused by Disease","author":"Tofts","year":"2005"},{"key":"10.1016\/j.media.2020.101945_bib0057","first-page":"3371","article-title":"Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.media.2020.101945_bib0058","series-title":"Image Processing (ICIP), 2017 IEEE International Conference on","first-page":"3953","article-title":"Better than real: complex-valued neural nets for MRI fingerprinting","author":"Virtue","year":"2017"},{"key":"10.1016\/j.media.2020.101945_bib0059","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1002\/(SICI)1522-2594(200005)43:5<682::AID-MRM10>3.0.CO;2-G","article-title":"Adaptive reconstruction of phased array MR imagery","volume":"43","author":"Walsh","year":"2000","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2020.101945_bib0060","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."},{"key":"10.1016\/j.media.2020.101945_bib0061","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1002\/jmri.24619","article-title":"Extended phase graphs: Dephasing, RF pulses, and echoes-pure and simple","volume":"41","author":"Weigel","year":"2015","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.media.2020.101945_bib0062","series-title":"Artificial Intelligence and Statistics","first-page":"370","article-title":"Deep kernel learning","author":"Wilson","year":"2016"},{"key":"10.1016\/j.media.2020.101945_bib0063","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.mri.2018.03.011","article-title":"Estimation of perfusion properties with MR fingerprinting arterial spin labeling","volume":"50","author":"Wright","year":"2018","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.media.2020.101945_bib0064","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/42.906424","article-title":"Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm","volume":"20","author":"Zhang","year":"2001","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2020.101945_bib0065","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1002\/mrm.26701","article-title":"Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling","volume":"79","author":"Zhao","year":"2018","journal-title":"Magn. Reson. Med."}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841520303091?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841520303091?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T18:45:11Z","timestamp":1761245111000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841520303091"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4]]},"references-count":65,"alternative-id":["S1361841520303091"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2020.101945","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2021,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2020.101945","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"101945"}}