{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:59:59Z","timestamp":1767322799109,"version":"3.48.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032113160","type":"print"},{"value":"9783032113177","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-11317-7_32","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:56:52Z","timestamp":1767322612000},"page":"381-392","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Supporting Cardiac MRI Generation with\u00a0High Fidelity Digital Twin"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4604-2006","authenticated-orcid":false,"given":"Giulio","family":"Del Corso","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1590-7890","authenticated-orcid":false,"given":"Claudia","family":"Caudai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2690-9998","authenticated-orcid":false,"given":"Roberto","family":"Verzicco","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1303-5934","authenticated-orcid":false,"given":"Francesco","family":"Viola","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2022-0804","authenticated-orcid":false,"given":"Sara","family":"Colantonio","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102688","volume":"84","author":"Y Al Khalil","year":"2023","unstructured":"Al Khalil, Y., Amirrajab, S., Lorenz, C., Weese, J., Pluim, J., Breeuwer, M.: On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic resonance images. Med. Image Analy. 84, 102688 (2023)","journal-title":"Med. Image Analy."},{"issue":"3","key":"32_CR2","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.media.2007.12.003","volume":"12","author":"A Andreopoulos","year":"2008","unstructured":"Andreopoulos, A., Tsotsos, J.K.: Efficient and generalizable statistical models of shape and appearance for analysis of cardiac mri. Med. Image Analy. 12(3), 335\u2013357 (2008)","journal-title":"Med. Image Analy."},{"key":"32_CR3","first-page":"1788","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M., Guttag, J., Dalca, A.V.: Voxelmorph: a learning framework for deformable medical image registration. IEEE TMI: Trans. Med. Imaging 38, 1788\u20131800 (2019)","journal-title":"IEEE TMI: Trans. Med. Imaging"},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Bansal, A., Ma, S., Ramanan, D., Sheikh, Y.: Recycle-GAN: unsupervised video retargeting. In: Proceedings of the European Conference On Computer Vision (ECCV), pp. 119\u2013135 (2018)","DOI":"10.1007\/978-3-030-01228-1_8"},{"issue":"11","key":"32_CR5","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514\u20132525 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"32_CR6","unstructured":"Chen, C., et al.: Ocmr (v1. 0)\u2013open-access multi-coil k-space dataset for cardiovascular magnetic resonance imaging. arXiv preprint arXiv:2008.03410 (2020)"},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: Ultrasound image-to-video synthesis via latent dynamic diffusion models. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 764\u2013774. Springer (2024)","DOI":"10.1007\/978-3-031-72083-3_71"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Del\u00a0Corso, G., Colantonio, S., Caudai, C.: Shedding light on uncertainties in machine learning: formal derivation and optimal model selection. J. Franklin Inst., 107548 (2025)","DOI":"10.1016\/j.jfranklin.2025.107548"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Del\u00a0Corso, G., et al.: Facial landmark identification and data preparation can significantly improve the extraction of newborns\u2019 facial features. In: 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), pp.\u00a01\u20137. IEEE (2024)","DOI":"10.1109\/FG59268.2024.10581971"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Del\u00a0Corso, G., Volpini, F., Caudai, C., Moroni, D., Colantonio, S.: Descriptor: not-a-database of synthetic shapes benchmarking dataset (nada-synshapes). IEEE Data Descriptions (2025)","DOI":"10.1109\/IEEEDATA.2025.3562805"},{"key":"32_CR11","first-page":"81","volume":"2018","author":"A DeSimone","year":"2020","unstructured":"DeSimone, A., et al.: Segregated algorithms for the numerical simulation of cardiac electromechanics in the left human ventricle. Math. Mechanobiol. Cetraro, Italy 2018, 81\u2013116 (2020)","journal-title":"Math. Mechanobiol. Cetraro, Italy"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Ghanbari, F., et\u00a0al.: Free-breathing, highly accelerated, single-beat, multisection cardiac cine MRI with generative artificial intelligence. Radiol. Cardiothoracic Imaging 7(2), e240272 (2025)","DOI":"10.1148\/ryct.240272"},{"issue":"9","key":"32_CR13","doi-asserted-by":"publisher","first-page":"3998","DOI":"10.1002\/mp.13656","volume":"46","author":"J Harms","year":"2019","unstructured":"Harms, J., et al.: Paired cycle-gan-based image correction for quantitative cone-beam computed tomography. Med. Phys. 46(9), 3998\u20134009 (2019)","journal-title":"Med. Phys."},{"key":"32_CR14","unstructured":"Henry, J., Natalie, T., Madsen, D.: Pix2pix GAN for image-to-image translation. Res. Gate Publ.\u00a01\u20135 (2021)"},{"key":"32_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.109834","volume":"189","author":"M Ibrahim","year":"2025","unstructured":"Ibrahim, M., et al.: Generative ai for synthetic data across multiple medical modalities: a systematic review of recent developments and challenges. Comput. Biol. Med. 189, 109834 (2025)","journal-title":"Comput. Biol. Med."},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Liu, C., Yuan, X., Yu, Z., Wang, Y.: Texdc: text-driven disease-aware 4d cardiac cine MRI images generation. In: Proceedings of the Asian Conference on Computer Vision, pp. 3005\u20133021 (2024)","DOI":"10.1007\/978-981-96-0901-7_12"},{"issue":"7","key":"32_CR17","first-page":"3302","volume":"27","author":"C Mart\u00edn-Isla","year":"2023","unstructured":"Mart\u00edn-Isla, C., et al.: Deep learning segmentation of the right ventricle in cardiac mri: the m &ms challenge. IEEE J. Biomed. Heal. Inf. 27(7), 3302\u20133313 (2023)","journal-title":"IEEE J. Biomed. Heal. Inf."},{"issue":"6","key":"32_CR18","doi-asserted-by":"publisher","first-page":"061403","DOI":"10.1117\/1.JMI.10.6.061403","volume":"10","author":"R Osuala","year":"2023","unstructured":"Osuala, R., et al.: medigan: a python library of pretrained generative models for medical image synthesis. J. Med. Imaging 10(6), 061403\u2013061403 (2023)","journal-title":"J. Med. Imaging"},{"key":"32_CR19","doi-asserted-by":"publisher","DOI":"10.2196\/48785","volume":"9","author":"C Preiksaitis","year":"2023","unstructured":"Preiksaitis, C., Rose, C.: Opportunities, challenges, and future directions of generative artificial intelligence in medical education: scoping review. JMIR Med. Educ. 9, e48785 (2023)","journal-title":"JMIR Med. Educ."},{"key":"32_CR20","doi-asserted-by":"crossref","unstructured":"Radau, P., Lu, Y., Connelly, K., Paul, G., Dick, A.J., Wright, G.A.: Evaluation framework for algorithms segmenting short axis cardiac MRI. MIDAS J. (2009)","DOI":"10.54294\/g80ruo"},{"issue":"3","key":"32_CR21","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1093\/ehjci\/jeaa297","volume":"22","author":"Z Raisi-Estabragh","year":"2021","unstructured":"Raisi-Estabragh, Z., Harvey, N.C., Neubauer, S., Petersen, S.E.: Cardiovascular magnetic resonance imaging in the uk biobank: a major international health research resource. European Heart J. Cardiovascular Imaging 22(3), 251\u2013258 (2021)","journal-title":"European Heart J. Cardiovascular Imaging"},{"issue":"3","key":"32_CR22","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/2945.795213","volume":"5","author":"H Ray","year":"1999","unstructured":"Ray, H., Pfister, H., Silver, D., Cook, T.A.: Ray casting architectures for volume visualization. IEEE Trans. Visualization Comput. Graph. 5(3), 210\u2013223 (1999)","journal-title":"IEEE Trans. Visualization Comput. Graph."},{"key":"32_CR23","doi-asserted-by":"crossref","unstructured":"Sizikova, E., et al.: Synthetic data in radiological imaging: current state and future outlook. BJR|Artifi.Intell. 1(1), ubae007 (05 2024)","DOI":"10.1093\/bjrai\/ubae007"},{"issue":"1","key":"32_CR24","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/s10916-024-02072-0","volume":"48","author":"MH Temsah","year":"2024","unstructured":"Temsah, M.H., et al.: Art or artifact: evaluating the accuracy, appeal, and educational value of ai-generated imagery in dall\u00b7 e 3 for illustrating congenital heart diseases. J. Med. Syst. 48(1), 54 (2024)","journal-title":"J. Med. Syst."},{"issue":"7","key":"32_CR25","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1038\/s42256-024-00864-0","volume":"6","author":"PD Tudosiu","year":"2024","unstructured":"Tudosiu, P.D., et al.: Realistic morphology-preserving generative modelling of the brain. Nat. Mach. Intell. 6(7), 811\u2013819 (2024)","journal-title":"Nat. Mach. Intell."},{"issue":"1","key":"32_CR26","first-page":"8230","volume":"13","author":"F Viola","year":"2023","unstructured":"Viola, F., Del Corso, G., De Paulis, R., Verzicco, R.: Gpu accelerated digital twins of the human heart open new routes for cardiovascular research. Sci. Reports 13(1), 8230 (2023)","journal-title":"Sci. Reports"},{"key":"32_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102678","volume":"83","author":"A Zakeri","year":"2023","unstructured":"Zakeri, A., et al.: Dragnet: learning-based deformable registration for realistic cardiac mr sequence generation from a single frame. Med. Image Anal. 83, 102678 (2023)","journal-title":"Med. Image Anal."},{"issue":"1","key":"32_CR28","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s00330-022-08971-5","volume":"33","author":"L Zhang","year":"2023","unstructured":"Zhang, L., et al.: Motion artifact removal in coronary ct angiography based on generative adversarial networks. European Radiol. 33(1), 43\u201353 (2023)","journal-title":"European Radiol."}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing - ICIAP 2025 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-11317-7_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:56:54Z","timestamp":1767322614000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-11317-7_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032113160","9783032113177"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-11317-7_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap.org\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}