{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T23:06:17Z","timestamp":1779059177064,"version":"3.51.4"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032177339","type":"print"},{"value":"9783032177346","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\/978-3-032-17734-6_27","type":"book-chapter","created":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T22:12:39Z","timestamp":1779055959000},"page":"287-298","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GENRE-CMR: Generalizable Deep Learning for\u00a0Diverse Multi-domain Cardiac MRI Reconstruction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7416-1987","authenticated-orcid":false,"given":"Kian Anvari","family":"Hamedani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8985-9653","authenticated-orcid":false,"given":"Narges","family":"Razizadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7240-0239","authenticated-orcid":false,"given":"Shahabedin","family":"Nabavi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7391-508X","authenticated-orcid":false,"given":"Mohsen Ebrahimi","family":"Moghaddam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"27_CR1","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1136\/heartjnl-2019-314856","volume":"106","author":"J Arnold","year":"2020","unstructured":"Arnold, J., McCann, G.: Cardiovascular magnetic resonance: applications and practical considerations for the general cardiologist. Heart 106, 174\u2013181 (2020)","journal-title":"Heart"},{"key":"27_CR2","doi-asserted-by":"publisher","first-page":"1755","DOI":"10.1136\/heartjnl-2018-312971","volume":"105","author":"M Vasquez","year":"2019","unstructured":"Vasquez, M., Nagel, E.: Clinical indications for cardiovascular magnetic resonance. Heart 105, 1755\u20131762 (2019)","journal-title":"Heart"},{"key":"27_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107770","volume":"242","author":"S Nabavi","year":"2023","unstructured":"Nabavi, S., Simchi, H., Moghaddam, M., Abin, A., Frangi, A.: A generalised deep meta-learning model for automated quality control of cardiovascular magnetic resonance images. Comput. Methods Programs Biomed. 242, 107770 (2023)","journal-title":"Comput. Methods Programs Biomed."},{"key":"27_CR4","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."},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Bi, N., et al.: SegMorph: concurrent motion estimation and segmentation for cardiac MRI sequences. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3435000"},{"key":"27_CR6","unstructured":"Kebriti, S., Nabavi, S., Gooya, A.: FractMorph: a fractional fourier-based multi-domain transformer for deformable image registration. arXiv Preprint ArXiv:2508.12445 (2025)"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Enders, J., et al.: Reduction of claustrophobia during magnetic resonance imaging: methods and design of the \u201cCLAUSTRO\u201d randomized controlled trial. BMC Med. Imaging 11, 1\u201315 (2011)","DOI":"10.1186\/1471-2342-11-4"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Cmrxrecon2024: a multimodality, multiview k-space dataset boosting universal machine learning for accelerated cardiac MRI. Radiol.: Artif. Intell. 7, e240443 (2025)","DOI":"10.1148\/ryai.259001"},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","volume":"69","author":"H Guan","year":"2021","unstructured":"Guan, H., Liu, M.: Domain adaptation for medical image analysis: a survey. IEEE Trans. Biomed. Eng. 69, 1173\u20131185 (2021)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Anvari Hamedani, K., Razizadeh, N., Nabavi, S., Ebrahimi Moghaddam, M.: An All-in-one approach for accelerated cardiac MRI reconstruction. In: International Workshop on Statistical Atlases and Computational Models of the Heart, pp. 464\u2013475 (2024)","DOI":"10.1007\/978-3-031-87756-8_45"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Xin, B., Ye, M., Axel, L., Metaxas, D.: Rethinking deep unrolled model for accelerated MRI reconstruction. In: European Conference on Computer Vision, pp. 164\u2013181 (2024)","DOI":"10.1007\/978-3-031-73226-3_10"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Xu, R., \u00d6zer, C., Oksuz, I.: Hypercmr: enhanced multi-contrast CMR reconstruction with eagle loss. IN: International Workshop on Statistical Atlases and Computational Models of the Heart, pp. 152\u2013163 (2024)","DOI":"10.1007\/978-3-031-87756-8_15"},{"key":"27_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105200","volume":"150","author":"R Xu","year":"2024","unstructured":"Xu, R., Oksuz, I.: Segmentation-aware MRI subsampling for efficient cardiac MRI reconstruction with reinforcement learning. Image Vision Comput. 150, 105200 (2024)","journal-title":"Image Vision Comput."},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Yiasemis, G., Moriakov, N., Sonke, J., Teuwen, J.: Deep multi-contrast cardiac MRI reconstruction via vsharp with auxiliary refinement network. In: International Workshop on Statistical Atlases and Computational Models of the Heart, pp. 183\u2013192 (2024)","DOI":"10.1007\/978-3-031-87756-8_18"},{"key":"27_CR15","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1038\/s41597-024-03525-4","volume":"11","author":"C Wang","year":"2024","unstructured":"Wang, C., et al.: CMRxRecon: a publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI. Sci. Data 11, 687 (2024)","journal-title":"Sci. Data"},{"key":"27_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103485","volume":"101","author":"J Lyu","year":"2025","unstructured":"Lyu, J., et al.: The state-of-the-art in cardiac MRI reconstruction: results of the CMRxRecon challenge in MICCAI 2023. Med. Image Anal. 101, 103485 (2025)","journal-title":"Med. Image Anal."},{"key":"27_CR17","unstructured":"Wang, F., et al.: Towards universal learning-based model for cardiac image reconstruction: summary of the CMRxRecon2024 challenge. arXiv Preprint ArXiv:2503.03971 (2025)"},{"key":"27_CR18","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S","volume":"42","author":"K Pruessmann","year":"1999","unstructured":"Pruessmann, K., Weiger, M., Scheidegger, M., Boesiger, P.: SENSE: sensitivity encoding for fast MRI. Magn. Resonan. Med. Off. J. Int. Soc. Magn. Resonan. Med. 42, 952\u2013962 (1999)","journal-title":"Magn. Resonan. Med. Off. J. Int. Soc. Magn. Resonan. Med."},{"key":"27_CR19","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1002\/mrm.10171","volume":"47","author":"M Griswold","year":"2002","unstructured":"Griswold, M., et al.: Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn. Resonan. Med. Off. J. Int. Soc. Magn. Resonan. Med. 47, 1202\u20131210 (2002)","journal-title":"Magn. Resonan. Med. Off. J. Int. Soc. Magn. Resonan. Med."},{"key":"27_CR20","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.: Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Resonan. Med. Off. J. Int. Soc. Magn. Resonan. Med. 58, 1182\u20131195 (2007)","journal-title":"Magn. Resonan. Med. Off. J. Int. Soc. Magn. Resonan. Med."},{"key":"27_CR21","unstructured":"Nabavi, S., Hamedani, K., Moghaddam, M., Abin, A., Frangi, A.: Multiple teachers-meticulous student: a domain adaptive meta-knowledge distillation model for medical image classification. arXiv Preprint arXiv:2403.11226 (2024)"},{"key":"27_CR22","unstructured":"Nabavi, S., Hamedani, K., Moghaddam, M., Abin, A., Frangi, A.: Statistical distance-guided unsupervised domain adaptation for automated multi-class cardiovascular magnetic resonance image quality assessment. arXiv Preprint arXiv:2409.00375 (2024)"},{"key":"27_CR23","unstructured":"Ouyang, C., et al.: Generalizing deep learning MRI reconstruction across different domains. arXiv Preprint arXiv:1902.10815 (2019)"},{"key":"27_CR24","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.3390\/bioengineering11121305","volume":"11","author":"C Millard","year":"2024","unstructured":"Millard, C., Chiew, M.: Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDU. Bioengineering 11, 1305 (2024)","journal-title":"Bioengineering"},{"key":"27_CR25","unstructured":"Gao, Z., Zhou, S.: MRPD: undersampled MRI reconstruction by prompting a large latent diffusion model. arXiv Preprint arXiv:2402.10609 (2024)"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Patel, J., Kadota, B., Sheagren, C., Chiew, M., Wright, G.: Low-rank conjugate gradient-net for accelerated cardiac MR imaging. In: International Workshop on Statistical Atlases and Computational Models of the Heart, pp. 334\u2013344 (2024)","DOI":"10.1007\/978-3-031-87756-8_33"},{"key":"27_CR27","unstructured":"Xu, D.: CMRxRecon2025. (IEEE Dataport, 2025). https:\/\/dx.doi.org\/10.21227\/b6xs-gv29"},{"key":"27_CR28","doi-asserted-by":"crossref","unstructured":"Liang, C., Chen, W., Zhao, X., Wang, J., Cao, L., Zhang, J.: Distribution optimization under gaussian hypothesis for domain adaptive semantic segmentation. 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 9280\u20139290 (2025)","DOI":"10.1109\/WACV61041.2025.00899"},{"key":"27_CR29","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41\u201348 (2009)","DOI":"10.1145\/1553374.1553380"},{"key":"27_CR30","doi-asserted-by":"crossref","unstructured":"Groenendijk, R., Karaoglu, S., Gevers, T., Mensink, T.: Multi-loss weighting with coefficient of variations. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1468\u20131477 (2021)","DOI":"10.1109\/WACV48630.2021.00151"},{"key":"27_CR31","doi-asserted-by":"crossref","unstructured":"Xin, B., Ye, M., Axel, L., Metaxas, D.: Fill the k-space and refine the image: prompting for dynamic and multi-contrast MRI reconstruction. In: International Workshop on Statistical Atlases and Computational Models of the Heart, pp. 261\u2013273 (2023)","DOI":"10.1007\/978-3-031-52448-6_25"}],"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\/978-3-032-17734-6_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T22:12:42Z","timestamp":1779055962000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-17734-6_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032177339","9783032177346"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-17734-6_27","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":"1 May 2026","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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"27 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"stacom2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/stacom.github.io\/stacom2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}