{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:30:31Z","timestamp":1776094231523,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031524479","type":"print"},{"value":"9783031524486","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-52448-6_8","type":"book-chapter","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T07:03:34Z","timestamp":1706771014000},"page":"77-87","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Contrast-Agnostic Groupwise Registration by\u00a0Robust PCA for\u00a0Quantitative Cardiac MRI"],"prefix":"10.1007","author":[{"given":"Xinqi","family":"Li","sequence":"first","affiliation":[]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yidong","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Jan","family":"van Gemert","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Tao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,2]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","first-page":"34","DOI":"10.3389\/fninf.2019.00034","volume":"13","author":"S Ahmad","year":"2019","unstructured":"Ahmad, S., Fan, J., Dong, P., Cao, X., Yap, P.T., Shen, D.: Deep learning deformation initialization for rapid groupwise registration of inhomogeneous image populations. Front. Neuroinform. 13, 34 (2019)","journal-title":"Front. Neuroinform."},{"issue":"1","key":"8_CR2","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","volume":"38","author":"J Ashburner","year":"2007","unstructured":"Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38(1), 95\u2013113 (2007)","journal-title":"Neuroimage"},{"issue":"1","key":"8_CR3","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","volume":"12","author":"BB Avants","year":"2008","unstructured":"Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26\u201341 (2008)","journal-title":"Med. Image Anal."},{"issue":"8","key":"8_CR4","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: Voxelmorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR5","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-030-52791-4_15","volume-title":"Medical Image Understanding and Analysis","author":"M Brudfors","year":"2020","unstructured":"Brudfors, M., Balbastre, Y., Ashburner, J.: Groupwise multimodal image registration using joint total variation. In: Papie\u017c, B.W., Namburete, A.I.L., Yaqub, M., Noble, J.A. (eds.) MIUA 2020. CCIS, vol. 1248, pp. 184\u2013194. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-52791-4_15"},{"issue":"3","key":"8_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1970392.1970395","volume":"58","author":"EJ Cand\u00e8s","year":"2011","unstructured":"Cand\u00e8s, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM (JACM) 58(3), 1\u201337 (2011)","journal-title":"J. ACM (JACM)"},{"key":"8_CR7","doi-asserted-by":"publisher","first-page":"27650","DOI":"10.1109\/ACCESS.2019.2901580","volume":"7","author":"T Che","year":"2019","unstructured":"Che, T., et al.: Deep group-wise registration for multi-spectral images from fundus images. IEEE Access 7, 27650\u201327661 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"8_CR8","first-page":"012003","volume":"3","author":"X Chen","year":"2021","unstructured":"Chen, X., Diaz-Pinto, A., Ravikumar, N., Frangi, A.F.: Deep learning in medical image registration. Prog. Biomed. Eng. 3(1), 012003 (2021)","journal-title":"Prog. Biomed. Eng."},{"issue":"6","key":"8_CR9","doi-asserted-by":"publisher","first-page":"2082","DOI":"10.1002\/mrm.24878","volume":"71","author":"K Chow","year":"2014","unstructured":"Chow, K., Flewitt, J.A., Green, J.D., Pagano, J.J., Friedrich, M.G., Thompson, R.B.: Saturation recovery single-shot acquisition (SASHA) for myocardial T1 mapping. Magn. Reson. Med. 71(6), 2082\u20132095 (2014)","journal-title":"Magn. Reson. Med."},{"issue":"7","key":"8_CR10","doi-asserted-by":"publisher","first-page":"2506","DOI":"10.1109\/TMI.2020.2972616","volume":"39","author":"T Fechter","year":"2020","unstructured":"Fechter, T., Baltas, D.: One-shot learning for deformable medical image registration and periodic motion tracking. IEEE Trans. Med. Imaging 39(7), 2506\u20132517 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR11","unstructured":"Feng, J., Xu, H., Yan, S.: Online robust PCA via stochastic optimization. In: Advances in Neural Information Processing Systems, vol. 26 (2013)"},{"issue":"1","key":"8_CR12","doi-asserted-by":"publisher","first-page":"34461","DOI":"10.1038\/srep34461","volume":"6","author":"Q Feng","year":"2016","unstructured":"Feng, Q., et al.: Liver DCE-MRI registration in manifold space based on robust principal component analysis. Sci. Rep. 6(1), 34461 (2016)","journal-title":"Sci. Rep."},{"issue":"4","key":"8_CR13","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1016\/j.neuroimage.2009.04.024","volume":"47","author":"X Geng","year":"2009","unstructured":"Geng, X., Christensen, G.E., Gu, H., Ross, T.J., Yang, Y.: Implicit reference-based group-wise image registration and its application to structural and functional MRI. Neuroimage 47(4), 1341\u20131351 (2009)","journal-title":"Neuroimage"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Gonzales, R., et al.: MOCOnet: robust motion correction of cardiovascular magnetic resonance T1 mapping using convolutional neural networks. Front. Cardiovasc. Med. 8, 768245 (2021)","DOI":"10.3389\/fcvm.2021.768245"},{"issue":"1","key":"8_CR15","doi-asserted-by":"publisher","first-page":"13112","DOI":"10.1038\/s41598-018-31474-7","volume":"8","author":"JM Guyader","year":"2018","unstructured":"Guyader, J.M., et al.: Groupwise image registration based on a total correlation dissimilarity measure for quantitative MRI and dynamic imaging data. Sci. Rep. 8(1), 13112 (2018)","journal-title":"Sci. Rep."},{"issue":"2","key":"8_CR16","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.media.2013.10.016","volume":"18","author":"V Hamy","year":"2014","unstructured":"Hamy, V., et al.: Respiratory motion correction in dynamic MRI using robust data decomposition registration-application to DCE-MRI. Med. Image Anal. 18(2), 301\u2013313 (2014)","journal-title":"Med. Image Anal."},{"key":"8_CR17","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.media.2015.12.004","volume":"29","author":"W Huizinga","year":"2016","unstructured":"Huizinga, W., et al.: PCA-based groupwise image registration for quantitative MRI. Med. Image Anal. 29, 65\u201378 (2016)","journal-title":"Med. Image Anal."},{"issue":"1","key":"8_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1532-429X-15-56","volume":"15","author":"P Kellman","year":"2013","unstructured":"Kellman, P., Arai, A.E., Xue, H.: T1 and extracellular volume mapping in the heart: estimation of error maps and the influence of noise on precision. J. Cardiovasc. Magn. Reson. 15(1), 1\u201312 (2013)","journal-title":"J. Cardiovasc. Magn. Reson."},{"issue":"3","key":"8_CR19","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/j.neuroimage.2008.12.037","volume":"46","author":"A Klein","year":"2009","unstructured":"Klein, A., et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46(3), 786\u2013802 (2009)","journal-title":"Neuroimage"},{"issue":"10","key":"8_CR20","doi-asserted-by":"publisher","first-page":"e4775","DOI":"10.1002\/nbm.4775","volume":"35","author":"Y Li","year":"2022","unstructured":"Li, Y., Wu, C., Qi, H., Si, D., Ding, H., Chen, H.: Motion correction for native myocardial T1 mapping using self-supervised deep learning registration with contrast separation. NMR Biomed. 35(10), e4775 (2022)","journal-title":"NMR Biomed."},{"issue":"9","key":"8_CR21","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1109\/TMI.2002.804441","volume":"21","author":"T Makela","year":"2002","unstructured":"Makela, T., et al.: A review of cardiac image registration methods. IEEE Trans. Med. Imaging 21(9), 1011\u20131021 (2002)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"8_CR22","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1002\/mrm.20110","volume":"52","author":"DR Messroghli","year":"2004","unstructured":"Messroghli, D.R., Radjenovic, A., Kozerke, S., Higgins, D.M., Sivananthan, M.U., Ridgway, J.P.: Modified look-locker inversion recovery (MOLLI) for high-resolution T1 mapping of the heart. Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med. 52(1), 141\u2013146 (2004)","journal-title":"Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med."},{"key":"8_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015 Part III. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"2S","key":"8_CR24","doi-asserted-by":"publisher","first-page":"S142","DOI":"10.1148\/radiol.14140432","volume":"273","author":"A de Roos","year":"2014","unstructured":"de Roos, A., Higgins, C.B.: Cardiac radiology: centenary review. Radiology 273(2S), S142\u2013S159 (2014)","journal-title":"Radiology"},{"issue":"8","key":"8_CR25","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/42.796284","volume":"18","author":"D Rueckert","year":"1999","unstructured":"Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712\u2013721 (1999)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"8_CR26","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1002\/jmri.25863","volume":"47","author":"Q Tao","year":"2018","unstructured":"Tao, Q., van der Tol, P., Berendsen, F.F., Paiman, E.H., Lamb, H.J., van der Geest, R.J.: Robust motion correction for myocardial T1 and extracellular volume mapping by principle component analysis-based groupwise image registration. J. Magn. Reson. Imaging 47(5), 1397\u20131405 (2018)","journal-title":"J. Magn. Reson. Imaging"},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"de Vos, B.D., van der Velden, B.H., Sander, J., Gilhuijs, K.G., Staring, M., I\u0161gum, I.: Mutual information for unsupervised deep learning image registration. In: Medical Imaging 2020: Image Processing, vol. 11313, pp. 155\u2013161. SPIE (2020)","DOI":"10.1117\/12.2549729"},{"issue":"6","key":"8_CR28","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1002\/mrm.23153","volume":"67","author":"H Xue","year":"2012","unstructured":"Xue, H., et al.: Motion correction for myocardial T1 mapping using image registration with synthetic image estimation. Magn. Reson. Med. 67(6), 1644\u20131655 (2012)","journal-title":"Magn. Reson. Med."},{"issue":"4","key":"8_CR29","doi-asserted-by":"publisher","first-page":"045030","DOI":"10.1088\/1361-6560\/abd956","volume":"66","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Wu, X., Gach, H.M., Li, H., Yang, D.: Groupregnet: a groupwise one-shot deep learning-based 4D image registration method. Phys. Med. Biol. 66(4), 045030 (2021)","journal-title":"Phys. Med. Biol."},{"key":"8_CR30","unstructured":"Zhou, T., Tao, D.: Godec: randomized low-rank & sparse matrix decomposition in noisy case. In: Proceedings of the 28th International Conference on Machine Learning. ICML 2011 (2011)"}],"container-title":["Lecture Notes in Computer Science","Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-52448-6_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T07:04:46Z","timestamp":1706771086000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-52448-6_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031524479","9783031524486"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-52448-6_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"STACOM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Statistical Atlases and Computational Models of the Heart","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"stacom2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/stacom.github.io\/stacom2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}