{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T02:59:45Z","timestamp":1790305185987,"version":"4.1.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032382351","type":"print"},{"value":"9783032382368","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:00:00Z","timestamp":1790380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:00:00Z","timestamp":1790380800000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-38236-8_45","type":"book-chapter","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T02:43:15Z","timestamp":1790304195000},"page":"469-478","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SHINE: An Entropy-Guided Digital Twin Framework for\u00a03D Myocardial Scar Reconstruction from\u00a0Sparse CMR"],"prefix":"10.1007","author":[{"given":"Dexiang","family":"Zong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Leng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru-San","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xulei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ching-Hui","family":"Sia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derek","family":"Hausenloy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,26]]},"reference":[{"issue":"14","key":"45_CR1","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1056\/NEJM195504072521405","volume":"252","author":"LB Lusted","year":"1955","unstructured":"Lusted, L.B.: Medical electronics. New Engl. J. Med. 252(14), 580\u2013585 (1955)","journal-title":"New Engl. J. Med."},{"issue":"11","key":"45_CR2","doi-asserted-by":"publisher","first-page":"5799","DOI":"10.1118\/1.3013555","volume":"35","author":"ML Giger","year":"2008","unstructured":"Giger, M.L., Chan, H.P., Boone, J.: Anniversary paper: history and status of CAD and quantitative image analysis: the role of medical physics and AAPM. Med. Phys. 35(11), 5799\u20135820 (2008)","journal-title":"Med. Phys."},{"issue":"2","key":"45_CR3","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.jacc.2010.07.043","volume":"57","author":"E Perez-David","year":"2011","unstructured":"Perez-David, E., Arenal, A., Rubio-Guivernau, J.L., et al.: Noninvasive identification of ventricular tachycardia-related conducting channels using contrast-enhanced magnetic resonance imaging in patients with chronic myocardial infarction. J. Am. Coll. Cardiol. 57(2), 184\u2013194 (2011)","journal-title":"J. Am. Coll. Cardiol."},{"issue":"6","key":"45_CR4","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, J.M.: Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58(6), 1182\u20131195 (2007)","journal-title":"Magn. Reson. Med."},{"issue":"8","key":"45_CR5","doi-asserted-by":"publisher","first-page":"4579","DOI":"10.1118\/1.4926428","volume":"42","author":"E Ukwatta","year":"2015","unstructured":"Ukwatta, E.: Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology. Med. Phys. 42(8), 4579\u20134590 (2015)","journal-title":"Med. Phys."},{"key":"45_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.111219","volume":"198","author":"A Sen","year":"2025","unstructured":"Sen, A., Rohrer, U., Bhagirath, P., et al.: Weakly supervised learning for scar reconstruction in personalized cardiac models: integrating 2D MRI to 3D anatomical models. Comput. Biol. Med. 198, 111219 (2025)","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"45_CR7","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TMI.2017.2760978","volume":"37","author":"J Schlemper","year":"2018","unstructured":"Schlemper, J., Caballero, J., Hajnal, J.V., et al.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imaging 37(2), 491\u2013503 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"45_CR8","unstructured":"Rahaman, N., Baratin, A., Arpit, D.: On the spectral bias of neural networks. In: Proceedings of the ICML, vol. 97, pp. 5301\u20135310 (2019)"},{"key":"45_CR9","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M.: NeRF: Representing scenes as neural radiance fields for view synthesis. In: Proceedings of the ECCV, pp. 405\u2013421 (2020)","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"45_CR10","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, Y., Zhang, Y., Liang, D.: Implicit neural representation for medical image reconstruction. Phys. Med. Biol. 70(12) (2025)","DOI":"10.1088\/1361-6560\/addfa5"},{"issue":"12","key":"45_CR11","doi-asserted-by":"publisher","first-page":"4236","DOI":"10.1109\/TMI.2024.3419780","volume":"43","author":"C Shen","year":"2024","unstructured":"Shen, C., Zhu, H., Zhou, Y., et al.: Continuous 3D myocardial motion tracking via echocardiography. IEEE Trans. Med. Imaging 43(12), 4236\u20134252 (2024)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"48","key":"45_CR12","doi-asserted-by":"publisher","first-page":"4556","DOI":"10.1093\/eurheartj\/ehaa159","volume":"41","author":"J Corral-Acero","year":"2020","unstructured":"Corral-Acero, J., Mezger, F., Jagoda, P., et al.: The \u2018digital twin\u2019 to enable the vision of precision cardiology. Eur. Heart J. 41(48), 4556\u20134564 (2020)","journal-title":"Eur. Heart J."},{"issue":"4","key":"45_CR13","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1016\/j.jcmg.2025.11.006","volume":"19","author":"S Leng","year":"2026","unstructured":"Leng, S., Guo, J., Alabed, S., et al.: Digital twin model predicts prognosis in pulmonary arterial hypertension. JACC Cardiovasc. Imaging 19(4), 565\u2013567 (2026)","journal-title":"JACC Cardiovasc. Imaging"},{"key":"45_CR14","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686\u2013707 (2019)","journal-title":"J. Comput. Phys."},{"key":"45_CR15","unstructured":"Sitzmann, V., Martel, J., Bergman, A.: Implicit neural representations with periodic activation functions. In: Proceedings of the NeurIPS, vol. 33, pp. 7462\u20137473 (2020)"},{"key":"45_CR16","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of the ICML, pp. 1050\u20131059 (2016)"},{"issue":"2","key":"45_CR17","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., et al.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"45_CR18","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1186\/s12968-019-0551-6","volume":"21","author":"CA Mauger","year":"2019","unstructured":"Mauger, C.A., Gilbert, K., Lee, A.M., et al.: Right ventricular shape and function: cardiovascular magnetic resonance reference morphology and biventricular risk factor morphometrics in UK Biobank. J. Cardiovasc. Magn. Reson. 21, 41 (2019)","journal-title":"J. Cardiovasc. Magn. Reson."},{"issue":"2","key":"45_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2025.100204","volume":"4","author":"A Sun","year":"2026","unstructured":"Sun, A., Chen, X., Wan, X., et al.: Deep learning reconstruction enhanced myocardial scar detection on late gadolinium enhancement MRI in patients with ventricular arrhythmias. Meta-Radiol. 4(2), 100204 (2026)","journal-title":"Meta-Radiol."},{"issue":"1","key":"45_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocmr.2026.102712","volume":"28","author":"P Yang","year":"2026","unstructured":"Yang, P., Leng, S., Zong, D., et al.: Prognostic value of end-to-end deep learning assessment of myocardial scar and microvascular obstruction on late gadolinium enhancement cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson. 28(1), 102712 (2026)","journal-title":"J. Cardiovasc. Magn. Reson."},{"issue":"7","key":"45_CR21","doi-asserted-by":"publisher","first-page":"2466","DOI":"10.1109\/TMI.2024.3367409","volume":"43","author":"L Li","year":"2024","unstructured":"Li, L., Camps, J., Wang, Z.J., et al.: Toward enabling cardiac digital twins of myocardial infarction using deep computational models for inverse inference. IEEE Trans. Med. Imaging 43(7), 2466\u20132478 (2024)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"45_CR22","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1038\/s41746-025-02163-3","volume":"9","author":"R Zou","year":"2025","unstructured":"Zou, R.: ScarElastic: continuous elasticity field modeling for myocardial scar delineation in LGE-CMR. NPJ Digit. Med. 9(1), 3 (2025)","journal-title":"NPJ Digit. Med."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-38236-8_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T02:43:17Z","timestamp":1790304197000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-38236-8_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,26]]},"ISBN":["9783032382351","9783032382368"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-38236-8_45","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,26]]},"assertion":[{"value":"26 September 2026","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","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2026\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}