{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T06:46:48Z","timestamp":1747118808454,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031752902"},{"type":"electronic","value":"9783031752919"}],"license":[{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-75291-9_13","type":"book-chapter","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T20:16:25Z","timestamp":1729887385000},"page":"164-177","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LaMoD: Latent Motion Diffusion Model for Myocardial Strain Generation"],"prefix":"10.1007","author":[{"given":"Jiarui","family":"Xing","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nivetha","family":"Jayakumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frederick H.","family":"Epstein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miaomiao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,26]]},"reference":[{"issue":"6","key":"13_CR1","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1093\/ehjci\/jez041","volume":"20","author":"MS Amzulescu","year":"2019","unstructured":"Amzulescu, M.S., et al.: Myocardial strain imaging: review of general principles, validation, and sources of discrepancies. Eur. Heart J. Cardiovasc. Imaging 20(6), 605\u2013619 (2019)","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Arnold, V.: Sur la g\u00e9om\u00e9trie diff\u00e9rentielle des groupes de lie de dimension infinie et ses applications \u00e0 l\u2019hydrodynamique des fluides parfaits. In: Annales de l\u2019institut Fourier, vol.\u00a016, pp. 319\u2013361 (1966)","DOI":"10.5802\/aif.233"},{"issue":"8","key":"13_CR3","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1200\/JCO.2006.09.7998","volume":"25","author":"JM Balter","year":"2007","unstructured":"Balter, J.M., Kessler, M.L.: Imaging and alignment for image-guided radiation therapy. J. Clin. Oncol. 25(8), 931\u2013937 (2007)","journal-title":"J. Clin. Oncol."},{"issue":"2","key":"13_CR4","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. Int. J. Comput. Vision 61(2), 139\u2013157 (2005)","journal-title":"Int. J. Comput. Vision"},{"key":"13_CR5","unstructured":"Chen, J., et\u00a0al.: 3D TransUNet: advancing medical image segmentation through vision transformers. arXiv preprint arXiv:2310.07781 (2023)"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Claus, P., Omar, A.M.S., Pedrizzetti, G., Sengupta, P.P., Nagel, E.: Tissue tracking technology for assessing cardiac mechanics: principles, normal values, and clinical applications. JACC Cardiovasc. Imaging 8(12), 1444\u20131460 (2015)","DOI":"10.1016\/j.jcmg.2015.11.001"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"13_CR8","unstructured":"Hinkle, J.: Jacobhinkle\/Lagomorph (2021). https:\/\/github.com\/jacobhinkle\/lagomorph"},{"key":"13_CR9","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840\u20136851 (2020)"},{"issue":"1\u20133","key":"13_CR10","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."},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Jayakumar, N., Hossain, T., Zhang, M.: SADIR: shape-aware diffusion models for 3D image reconstruction. arXiv preprint arXiv:2309.03335 (2023)","DOI":"10.1007\/978-3-031-46914-5_23"},{"key":"13_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"13_CR13","doi-asserted-by":"publisher","first-page":"S19","DOI":"10.1016\/j.neuroimage.2004.07.021","volume":"23","author":"MI Miller","year":"2004","unstructured":"Miller, M.I.: Computational anatomy: shape, growth, and atrophy comparison via diffeomorphisms. Neuroimage 23, S19\u2013S33 (2004)","journal-title":"Neuroimage"},{"issue":"2","key":"13_CR14","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s10851-005-3624-0","volume":"24","author":"MI Miller","year":"2006","unstructured":"Miller, M.I., Trouv\u00e9, A., Younes, L.: Geodesic shooting for computational anatomy. J. Math. Imaging Vision 24(2), 209\u2013228 (2006)","journal-title":"J. Math. Imaging Vision"},{"key":"13_CR15","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2021.730316","volume":"8","author":"MA Morales","year":"2021","unstructured":"Morales, M.A., et al.: DeepStrain: a deep learning workflow for the automated characterization of cardiac mechanics. Front. Cardiovasc. Med. 8, 730316 (2021)","journal-title":"Front. Cardiovasc. Med."},{"issue":"1","key":"13_CR16","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s12968-016-0269-7","volume":"18","author":"G Pedrizzetti","year":"2016","unstructured":"Pedrizzetti, G., Claus, P., Kilner, P.J., Nagel, E.: Principles of cardiovascular magnetic resonance feature tracking and echocardiographic speckle tracking for informed clinical use. J. Cardiovasc. Magn. Reson. 18(1), 51 (2016)","journal-title":"J. Cardiovasc. Magn. Reson."},{"issue":"12","key":"13_CR17","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1016\/j.echo.2008.09.011","volume":"21","author":"ZB Popovi\u0107","year":"2008","unstructured":"Popovi\u0107, Z.B., et al.: Association between regional ventricular function and myocardial fibrosis in hypertrophic cardiomyopathy assessed by speckle tracking echocardiography and delayed hyperenhancement magnetic resonance imaging. J. Am. Soc. Echocardiogr. 21(12), 1299\u20131305 (2008)","journal-title":"J. Am. Soc. Echocardiogr."},{"issue":"9","key":"13_CR18","doi-asserted-by":"publisher","first-page":"4189","DOI":"10.1002\/mp.14341","volume":"47","author":"M Qiao","year":"2020","unstructured":"Qiao, M., Wang, Y., Guo, Y., Huang, L., Xia, L., Tao, Q.: Temporally coherent cardiac motion tracking from cine MRI: traditional registration method and modern CNN method. Med. Phys. 47(9), 4189\u20134198 (2020)","journal-title":"Med. Phys."},{"key":"13_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-3-030-00129-2_7","volume-title":"Machine Learning for Medical Image Reconstruction","author":"C Qin","year":"2018","unstructured":"Qin, C., et al.: Joint motion estimation and segmentation from undersampled cardiac MR image. In: Knoll, F., Maier, A., Rueckert, D. (eds.) MLMIR 2018. LNCS, vol. 11074, pp. 55\u201363. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00129-2_7"},{"key":"13_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102682","volume":"83","author":"C Qin","year":"2023","unstructured":"Qin, C., Wang, S., Chen, C., Bai, W., Rueckert, D.: Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior. Med. Image Anal. 83, 102682 (2023)","journal-title":"Med. Image Anal."},{"key":"13_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1007\/978-3-030-59716-0_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"C Qin","year":"2020","unstructured":"Qin, C., Wang, S., Chen, C., Qiu, H., Bai, W., Rueckert, D.: Biomechanics-informed neural networks for myocardial motion tracking in MRI. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 296\u2013306. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_29"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models (2021)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"13_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. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"13_CR24","unstructured":"Seo, D., Ho, J., Traverse, J.H., Forder, J., Vemuri, B.: Computing diffeomorphic paths with applications to cardiac motion analysis. In: 4th MICCAI Workshop on Mathematical Foundations of Computational Anatomy, pp. 83\u201394. Citeseer (2013)"},{"issue":"2","key":"13_CR25","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1586\/erc.12.182","volume":"11","author":"M Tee","year":"2013","unstructured":"Tee, M., Noble, J.A., Bluemke, D.A.: Imaging techniques for cardiac strain and deformation: comparison of echocardiography, cardiac magnetic resonance and cardiac computed tomography. Expert Rev. Cardiovasc. Ther. 11(2), 221\u2013231 (2013)","journal-title":"Expert Rev. Cardiovasc. Ther."},{"key":"13_CR26","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"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Wang, Y., et\u00a0al.: StrainNet: improved myocardial strain analysis of cine MRI by deep learning from dense. Radiol. Cardiothorac. Imaging 5(3), e220196 (2023)","DOI":"10.1148\/ryct.220196"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, M., Bilchick, K., Epstein, F.: TranSstrainNet: improved strain analysis of cine MRI with long-range spatiotemporal relationship learning. J. Cardiovasc. Mag. Reson. 26 (2024)","DOI":"10.1016\/j.jocmr.2024.100213"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Xing, J., Ghadimi, S., Abdi, M., Bilchick, K.C., Epstein, F.H., Zhang, M.: Deep networks to automatically detect late-activating regions of the heart. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1902\u20131906. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9433796"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Xing, J., Wang, S., Bilchick, K.C., Epstein, F.H., Patel, A.R., Zhang, M.: Multitask learning for improved late mechanical activation detection of heart from cine dense MRI. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230782"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Xing, J., Wu, N., Bilchick, K., Epstein, F., Zhang, M.: Multimodal learning to improve cardiac late mechanical activation detection from cine MR images. arXiv preprint arXiv:2402.18507 (2024)","DOI":"10.1109\/ISBI56570.2024.10635410"},{"issue":"6","key":"13_CR32","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.1002\/mrm.23142","volume":"67","author":"AA Young","year":"2012","unstructured":"Young, A.A., Li, B., Kirton, R.S., Cowan, B.R.: Generalized spatiotemporal myocardial strain analysis for DENSE and SPAMM imaging. Magn. Reson. Med. 67(6), 1590\u20131599 (2012)","journal-title":"Magn. Reson. Med."},{"key":"13_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-319-10443-0_16","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2014","author":"M Zhang","year":"2014","unstructured":"Zhang, M., Fletcher, P.T.: Bayesian principal geodesic analysis in diffeomorphic image registration. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 121\u2013128. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10443-0_16"},{"key":"13_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/978-3-319-19992-4_19","volume-title":"Information Processing in Medical Imaging","author":"M Zhang","year":"2015","unstructured":"Zhang, M., Fletcher, P.T.: Finite-dimensional lie algebras for fast diffeomorphic image registration. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 249\u2013260. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-19992-4_19"},{"key":"13_CR35","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-031-23443-9_2","volume-title":"STACOM 2022","author":"X Zhang","year":"2022","unstructured":"Zhang, X., You, C., Ahn, S., Zhuang, J., Staib, L., Duncan, J.: Learning correspondences of cardiac motion from images using biomechanics-informed modeling. In: Camara, O., et al. (eds.) STACOM 2022. LNCS, vol. 13593, pp. 13\u201325. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-23443-9_2"}],"container-title":["Lecture Notes in Computer Science","Shape in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-75291-9_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T20:17:19Z","timestamp":1729887439000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-75291-9_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,26]]},"ISBN":["9783031752902","9783031752919"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-75291-9_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,26]]},"assertion":[{"value":"26 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ShapeMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Shape in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"shapemi2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/shapemi.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}