{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:22:01Z","timestamp":1742912521393,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031474248"},{"type":"electronic","value":"9783031474255"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-47425-5_3","type":"book-chapter","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T22:02:10Z","timestamp":1706911330000},"page":"24-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MomentaMorph: Unsupervised Spatial-Temporal Registration with\u00a0Momenta, Shooting, and\u00a0Correction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3603-0650","authenticated-orcid":false,"given":"Zhangxing","family":"Bian","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8679-9615","authenticated-orcid":false,"given":"Shuwen","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Yihao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Junyu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jiachen","family":"Zhuo","sequence":"additional","affiliation":[]},{"given":"Fangxu","family":"Xing","sequence":"additional","affiliation":[]},{"given":"Jonghye","family":"Woo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4939-5085","authenticated-orcid":false,"given":"Aaron","family":"Carass","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6553-0876","authenticated-orcid":false,"given":"Jerry L.","family":"Prince","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,3]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","unstructured":"Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-Euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924\u2013931. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11866565_113","DOI":"10.1007\/11866565_113"},{"issue":"2","key":"3_CR2","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1148\/radiology.172.2.2748813","volume":"172","author":"L Axel","year":"1989","unstructured":"Axel, L., Dougherty, L.: Heart wall motion: improved method of spatial modulation of magnetization for MR imaging. Radiology 172(2), 349\u2013350 (1989)","journal-title":"Radiology"},{"issue":"3","key":"3_CR3","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1148\/radiology.171.3.2717762","volume":"171","author":"L Axel","year":"1989","unstructured":"Axel, L., Dougherty, L.: MR imaging of motion with spatial modulation of magnetization. Radiology 171(3), 841\u2013845 (1989)","journal-title":"Radiology"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Baker, H.F.: Abel\u2019s Theorem and the Allied Theory: Including the Theory of the Theta Functions. University Press (1897)","DOI":"10.3792\/chmm\/1428686976"},{"issue":"8","key":"3_CR5","first-page":"1788","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 TMI 38(8), 1788\u20131800 (2019)","journal-title":"IEEE TMI"},{"key":"3_CR6","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1023\/B:VISI.0000043755.93987.aa","volume":"61","author":"MF Beg","year":"2005","unstructured":"Beg, M.F., Miller, M.I., Trouv\u00e9, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. IJCV 61, 139\u2013157 (2005)","journal-title":"IJCV"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Bian, Z., Jabri, A., Efros, A.A., Owens, A.: Learning pixel trajectories with multiscale contrastive random walks. In: CVPR, pp. 6508\u20136519 (2022)","DOI":"10.1109\/CVPR52688.2022.00640"},{"key":"3_CR8","unstructured":"Bian, Z., et al.: Drimet: deep registration for 3d incompressible motion estimation in tagged-MRI with application to the tongue. arXiv preprint arXiv:2301.07234 (2023)"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Bian, Z., Zhong, J., Hatt, C.R., Burris, N.S.: A deformable image registration based method to assess directionality of thoracic aortic aneurysm growth. In: Medical Imaging 2021: Image Processing, vol. 11596, pp. 724\u2013731. SPIE (2021)","DOI":"10.1117\/12.2581937"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Burris, N.S., et al.: Vascular deformation mapping for CT surveillance of thoracic aortic aneurysm growth. Radiology 302(1), 218\u2013225 (2022)","DOI":"10.1148\/radiol.2021210658"},{"key":"3_CR11","unstructured":"Campbell, J.E.: A Course of Differential Geometry. Clarendon Press (1926)"},{"key":"3_CR12","unstructured":"Chen, J., et al.: A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond. arXiv preprint arXiv:2307.15615 (2023)"},{"issue":"10","key":"3_CR13","first-page":"1435","volume":"5","author":"GE Christensen","year":"1996","unstructured":"Christensen, G.E., Rabbitt, R.D., Miller, M.I.: Deformable templates using large deformation kinematics. IEEE TMI 5(10), 1435\u20131447 (1996)","journal-title":"IEEE TMI"},{"key":"3_CR14","doi-asserted-by":"publisher","unstructured":"Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning for fast probabilistic diffeomorphic registration. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 729\u2013738. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_82","DOI":"10.1007\/978-3-030-00928-1_82"},{"key":"3_CR15","doi-asserted-by":"publisher","unstructured":"Hall, B.C.: Lie groups, lie algebras, and representations. In: Quantum Theory for Mathematicians. GTM, vol. 267, pp. 333\u2013366. Springer, New York (2013). https:\/\/doi.org\/10.1007\/978-1-4614-7116-5_16","DOI":"10.1007\/978-1-4614-7116-5_16"},{"key":"3_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102139","volume":"72","author":"A Hering","year":"2021","unstructured":"Hering, A., H\u00e4ger, S., Moltz, J., Lessmann, N., Heldmann, S., van Ginneken, B.: CNN-based lung CT registration with multiple anatomical constraints. Med. Image Anal. 72, 102139 (2021)","journal-title":"Med. Image Anal."},{"issue":"1\u20133","key":"3_CR17","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","volume":"17","author":"BK Horn","year":"1981","unstructured":"Horn, B.K., Schunck, B.G.: Determining optical flow. Artif. Intell. 17(1\u20133), 185\u2013203 (1981)","journal-title":"Artif. Intell."},{"issue":"1","key":"3_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1532-429X-13-36","volume":"13","author":"ESH Ibrahim","year":"2011","unstructured":"Ibrahim, E.S.H.: Myocardial tagging by cardiovascular magnetic resonance: evolution of techniques-pulse sequences, analysis algorithms, and applications. J. Cardiovasc. Magn. Reson. 13(1), 1\u201340 (2011)","journal-title":"J. Cardiovasc. Magn. Reson."},{"issue":"1","key":"3_CR19","first-page":"193","volume":"49","author":"M Jenkinson","year":"2003","unstructured":"Jenkinson, M.: Fast, automated. N-dimensional phase-unwrapping algorithm. Mag. Reson. Med. 49(1), 193\u2013197 (2003)","journal-title":"N-dimensional phase-unwrapping algorithm. Mag. Reson. Med."},{"key":"3_CR20","doi-asserted-by":"publisher","unstructured":"Jia, X., Bartlett, J., Zhang, T., Lu, W., Qiu, Z., Duan, J.: U-net vs transformer: is U-net outdated in medical image registration? In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds.) MICCAI 2022, pp. 151\u2013160. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-21014-3_16","DOI":"10.1007\/978-3-031-21014-3_16"},{"key":"3_CR21","doi-asserted-by":"publisher","unstructured":"Jonschkowski, R., Stone, A., Barron, J.T., Gordon, A., Konolige, K., Angelova, A.: What matters in unsupervised optical flow. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 557\u2013572. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_33","DOI":"10.1007\/978-3-030-58536-5_33"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Knutsen, A.K., et al.: Improved measurement of brain deformation during mild head acceleration using a novel tagged MRI sequence. J. Biomech. 47(14), 3475\u20133481 (2014)","DOI":"10.1016\/j.jbiomech.2014.09.010"},{"issue":"8","key":"3_CR23","first-page":"1560","volume":"29","author":"X Liu","year":"2010","unstructured":"Liu, X., Prince, J.L.: Shortest path refinement for motion estimation from tagged MR images. IEEE TMI 29(8), 1560\u20131572 (2010)","journal-title":"IEEE TMI"},{"key":"3_CR24","doi-asserted-by":"publisher","unstructured":"Liu, Y., Zuo, L., Han, S., Xue, Y., Prince, J.L., Carass, A.: Coordinate translator for learning deformable medical image registration. In: Li, X., Lv, J., Huo, Y., Dong, B., Leahy, R.M., Li, Q. (eds.) Multiscale Multimodal Medical Imaging: Third International Workshop, MMMI 2022, Held in Conjunction with MICCAI 2022, pp. 98\u2013109. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-18814-5_10","DOI":"10.1007\/978-3-031-18814-5_10"},{"issue":"1","key":"3_CR25","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/s11263-010-0405-z","volume":"92","author":"T Mansi","year":"2011","unstructured":"Mansi, T., Pennec, X., Sermesant, M., Delingette, H., Ayache, N.: iLogDemons: a demons-based registration algorithm for tracking incompressible elastic biological tissues. Int. J. Comput. Vision 92(1), 92\u2013111 (2011)","journal-title":"Int. J. Comput. Vision"},{"issue":"6","key":"3_CR26","doi-asserted-by":"publisher","first-page":"1048","DOI":"10.1002\/(SICI)1522-2594(199912)42:6<1048::AID-MRM9>3.0.CO;2-M","volume":"42","author":"NF Osman","year":"1999","unstructured":"Osman, N.F., Kerwin, W.S., McVeigh, E.R., Prince, J.L.: Cardiac motion tracking using CINE harmonic phase (HARP) magnetic resonance imaging. Mag. Reson. Med. 42(6), 1048\u20131060 (1999)","journal-title":"Mag. Reson. Med."},{"issue":"6","key":"3_CR27","first-page":"730","volume":"22","author":"T Rohlfing","year":"2003","unstructured":"Rohlfing, T., Maurer, C.R., Bluemke, D.A., Jacobs, M.A.: Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint. IEEE TMI 22(6), 730\u2013741 (2003)","journal-title":"IEEE TMI"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Shao, M., et al.: Analysis of tongue muscle strain during speech from multimodal magnetic resonance imaging. J. Speech Lang. Hear. Res. 66(2), 513\u2013526 (2023)","DOI":"10.1044\/2022_JSLHR-22-00329"},{"issue":"1","key":"3_CR29","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1109\/LSP.2018.2879184","volume":"26","author":"G Spoorthi","year":"2018","unstructured":"Spoorthi, G., Gorthi, S., Gorthi, R.K.S.S.: Phasenet: a deep convolutional neural network for two-dimensional phase unwrapping. IEEE Signal Process. Lett. 26(1), 54\u201358 (2018)","journal-title":"IEEE Signal Process. Lett."},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Stone, A., Maurer, D., Ayvaci, A., Angelova, A., Jonschkowski, R.: Smurf: self-teaching multi-frame unsupervised raft with full-image warping. In: CVPR, pp. 3887\u20133896 (2021)","DOI":"10.1109\/CVPR46437.2021.00388"},{"key":"3_CR31","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s11263-011-0481-8","volume":"97","author":"FX Vialard","year":"2012","unstructured":"Vialard, F.X., Risser, L., Rueckert, D., Cotter, C.J.: Diffeomorphic 3d image registration via geodesic shooting using an efficient adjoint calculation. Int. J. Comput. Vision 97, 229\u2013241 (2012)","journal-title":"Int. J. Comput. Vision"},{"issue":"1","key":"3_CR32","doi-asserted-by":"publisher","DOI":"10.1117\/1.APN.1.1.014001","volume":"1","author":"K Wang","year":"2022","unstructured":"Wang, K., Kemao, Q., Di, J., Zhao, J.: Deep learning spatial phase unwrapping: a comparative review. Adv. Photon. Nexus 1(1), 014001 (2022)","journal-title":"Adv. Photon. Nexus"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Xing, F., et al.: Phase vector incompressible registration algorithm for motion estimation from tagged magnetic resonance images. IEEE TMI 36(10), 2116\u20132128 (2017)","DOI":"10.1109\/TMI.2017.2723021"},{"key":"3_CR34","doi-asserted-by":"crossref","unstructured":"Ye, M., et al.: Deeptag: an unsupervised deep learning method for motion tracking on cardiac tagging magnetic resonance images. In: CVPR, pp. 7261\u20137271 (2021)","DOI":"10.1109\/CVPR46437.2021.00718"},{"key":"3_CR35","doi-asserted-by":"crossref","unstructured":"Yu, J., et al.: New starting point registration method for tagged MRI tongue motion estimation. In: Medical Imaging 2023: Image Processing. SPIE (2023)","DOI":"10.1117\/12.2653913"},{"key":"3_CR36","unstructured":"Zhang, M., Lucas, J., Ba, J., Hinton, G.E.: Lookahead optimizer: k steps forward, 1 step back. Adv. Neural Inf. Process. Syst. 32 (2019)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47425-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T22:02:37Z","timestamp":1706911357000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47425-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031474248","9783031474255"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47425-5_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"3 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","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":"8 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":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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":"3","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":"5","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)"}}]}}