{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T22:06:55Z","timestamp":1779055615067,"version":"3.51.4"},"publisher-location":"Cham","reference-count":12,"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_30","type":"book-chapter","created":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T22:04:23Z","timestamp":1779055463000},"page":"323-331","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["KFDualUNet: Enhancing MRI Reconstruction Generalization Through Cross-Domain Synergistic Learning with\u00a0Dual Cascaded UNets and\u00a0K-Space Physical Constraints"],"prefix":"10.1007","author":[{"given":"Ye","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongjun","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"issue":"6","key":"30_CR1","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. Resonan. Med. Off. J. Int. Soc. Magn. Resonan. Med. 58(6), 1182\u20131195 (2007)","journal-title":"Magn. Resonan. Med. Off. J. Int. Soc. Magn. Resonan. Med."},{"key":"30_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102479","volume":"80","author":"H Chung","year":"2022","unstructured":"Chung, H., Ye, J.C.: Score-based diffusion models for accelerated MRI. Med. Image Anal. 80, 102479 (2022)","journal-title":"Med. Image Anal."},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"Li, G., Lv, J., Tian, Y., et al.: Transformer-empowered multi-scale contextual matching and aggregation for multi-contrast MRI super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20636\u201320645 (2022)","DOI":"10.1109\/CVPR52688.2022.01998"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Li, G., Rao, C., Mo, J., et al.: Rethinking diffusion model for multi-contrast MRI super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11365\u201311374 (2024)","DOI":"10.1109\/CVPR52733.2024.01080"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Cmrxrecon2024: a multimodality, multiview k-space dataset boosting universal machine learning for accelerated cardiac MRI. In: Radiol. Artif. Intell. 7(2), e240443 (2025)","DOI":"10.1148\/ryai.259001"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: A multi-modality, multi-view k-space dataset boosting universal machine learning for cardiac MRI reconstruction. J. Cardiovasc. Magn. Resonan. 27 (2025)","DOI":"10.1016\/j.jocmr.2024.101313"},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Qin, C., et al.: Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imaging 38(1), 280\u2013290 (2018)","DOI":"10.1109\/TMI.2018.2863670"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: Progressive divide-and-conquer via subsampling decomposition for accelerated MRI. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)","DOI":"10.1109\/CVPR52733.2024.02374"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Yiasemis, G., et al.: Recurrent variational network: a deep learning inverse problem solver applied to the task of accelerated MRI reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.00081"},{"key":"30_CR10","doi-asserted-by":"crossref","unstructured":"Guo, P., et al.: Reconformer: accelerated MRI reconstruction using recurrent transformer. IEEE Trans. Med. Imaging 43(1), 582\u2013593 (2023)","DOI":"10.1109\/TMI.2023.3314747"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, W., et al.: Fast controllable diffusion models for undersampled MRI reconstruction. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE (2024)","DOI":"10.1109\/ISBI56570.2024.10635891"}],"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_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T22:04:25Z","timestamp":1779055465000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-17734-6_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032177339","9783032177346"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-17734-6_30","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"}}]}}