{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T06:18:45Z","timestamp":1758349125046,"version":"3.44.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049360"},{"type":"electronic","value":"9783032049377"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-04937-7_48","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:40:12Z","timestamp":1758260412000},"page":"506-515","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ResMAP: Restoring MRIs of\u00a0Mixed Artifacts by\u00a0Prompt Cascading Retrieval"],"prefix":"10.1007","author":[{"given":"Yuxian","family":"Tang","sequence":"first","affiliation":[]},{"given":"Feng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"issue":"6","key":"48_CR1","doi-asserted-by":"publisher","first-page":"926","DOI":"10.3390\/diagnostics11060926","volume":"11","author":"B Callewaert","year":"2021","unstructured":"Callewaert, B., Jones, E.A., Himmelreich, U., Gsell, W.: Non-invasive evaluation of cerebral microvasculature using pre-clinical mri: principles, advantages and limitations. Diagnostics 11(6), 926 (2021)","journal-title":"Diagnostics"},{"issue":"1","key":"48_CR2","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1007\/s10278-022-00721-9","volume":"36","author":"Z Chen","year":"2023","unstructured":"Chen, Z., Pawar, K., Ekanayake, M., Pain, C., Zhong, S., Egan, G.F.: Deep learning for image enhancement and correction in magnetic resonance imaging\u2013state-of-the-art and challenges. J. Digit. Imaging 36(1), 204\u2013230 (2023)","journal-title":"J. Digit. Imaging"},{"key":"48_CR3","doi-asserted-by":"crossref","unstructured":"Conde, M.V., Geigle, G., Timofte, R.: Instructir: high-quality image restoration following human instructions. In: European Conference on Computer Vision, pp. 1\u201321. Springer (2024)","DOI":"10.1007\/978-3-031-72764-1_1"},{"issue":"10","key":"48_CR4","doi-asserted-by":"publisher","first-page":"3333","DOI":"10.1109\/TPAMI.2020.2984244","volume":"43","author":"X Deng","year":"2020","unstructured":"Deng, X., Dragotti, P.L.: Deep convolutional neural network for multi-modal image restoration and fusion. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3333\u20133348 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"48_CR5","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.ejrad.2007.11.005","volume":"65","author":"O Dietrich","year":"2008","unstructured":"Dietrich, O., Reiser, M.F., Schoenberg, S.O.: Artifacts in 3-t mri: physical background and reduction strategies. Eur. J. Radiol. 65(1), 29\u201335 (2008)","journal-title":"Eur. J. Radiol."},{"key":"48_CR6","unstructured":"Duffy, B.A., et al.: Retrospective correction of motion artifact affected structural mri images using deep learning of simulated motion. In: Medical Imaging with Deep Learning (2018)"},{"key":"48_CR7","doi-asserted-by":"publisher","first-page":"117756","DOI":"10.1016\/j.neuroimage.2021.117756","volume":"230","author":"BA Duffy","year":"2021","unstructured":"Duffy, B.A., et al.: Retrospective motion artifact correction of structural mri images using deep learning improves the quality of cortical surface reconstructions. Neuroimage 230, 117756 (2021)","journal-title":"Neuroimage"},{"key":"48_CR8","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.cmpb.2018.01.025","volume":"158","author":"E Gibson","year":"2018","unstructured":"Gibson, E., et al.: Niftynet: a deep-learning platform for medical imaging. Comput. Methods Programs Biomed. 158, 113\u2013122 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"key":"48_CR9","doi-asserted-by":"crossref","unstructured":"Goldfryd, T., Gordon, S., Raviv, T.R.: Deep semi-supervised bias field correction of mr images. In: 2021 IEEE 18th international symposium on biomedical imaging (ISBI), pp. 1836\u20131840. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9433889"},{"key":"48_CR10","doi-asserted-by":"crossref","unstructured":"Jiang, J., Zuo, Z., Wu, G., Jiang, K., Liu, X.: A survey on all-in-one image restoration: taxonomy, evaluation and future trends. arXiv preprint arXiv:2410.15067 (2024)","DOI":"10.1109\/TPAMI.2025.3598132"},{"key":"48_CR11","doi-asserted-by":"crossref","unstructured":"Jiang, N., Huang, Z., Sui, Y.: Explanation-driven cyclic learning for high-quality brain mri reconstruction from unknown degradation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 318\u2013328. Springer (2024)","DOI":"10.1007\/978-3-031-72104-5_31"},{"issue":"3","key":"48_CR12","doi-asserted-by":"publisher","first-page":"195","DOI":"10.2463\/mrms.mp.2019-0018","volume":"19","author":"M Kidoh","year":"2020","unstructured":"Kidoh, M., et al.: Deep learning based noise reduction for brain mr imaging: tests on phantoms and healthy volunteers. Magn. Reson. Med. Sci. 19(3), 195\u2013206 (2020)","journal-title":"Magn. Reson. Med. Sci."},{"issue":"1","key":"48_CR13","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1631\/FITEE.2300389","volume":"25","author":"Y Lei","year":"2024","unstructured":"Lei, Y., Li, J., Li, Z., Cao, Y., Shan, H.: Prompt learning in computer vision: a survey. Front. Inf. Technol. Electron. Eng. 25(1), 42\u201363 (2024)","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"48_CR14","doi-asserted-by":"crossref","unstructured":"Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17452\u201317462 (2022)","DOI":"10.1109\/CVPR52688.2022.01693"},{"issue":"1","key":"48_CR15","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1038\/s41597-023-02181-4","volume":"10","author":"M Lyu","year":"2023","unstructured":"Lyu, M., et al.: M4raw: a multi-contrast, multi-repetition, multi-channel mri k-space dataset for low-field mri research. Sci. Data 10(1), 264 (2023)","journal-title":"Sci. Data"},{"key":"48_CR16","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s10462-012-9338-y","volume":"42","author":"S Masoudnia","year":"2014","unstructured":"Masoudnia, S., Ebrahimpour, R.: Mixture of experts: a literature survey. Artif. Intell. Rev. 42, 275\u2013293 (2014)","journal-title":"Artif. Intell. Rev."},{"key":"48_CR17","doi-asserted-by":"publisher","first-page":"106236","DOI":"10.1016\/j.cmpb.2021.106236","volume":"208","author":"F P\u00e9rez-Garc\u00eda","year":"2021","unstructured":"P\u00e9rez-Garc\u00eda, F., Sparks, R., Ourselin, S.: Torchio: a python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Comput. Methods Programs Biomed. 208, 106236 (2021)","journal-title":"Comput. Methods Programs Biomed."},{"key":"48_CR18","first-page":"71275","volume":"36","author":"V Potlapalli","year":"2023","unstructured":"Potlapalli, V., Zamir, S.W., Khan, S.H., Shahbaz Khan, F.: Promptir: prompting for all-in-one image restoration. Adv. Neural. Inf. Process. Syst. 36, 71275\u201371293 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"4","key":"48_CR19","doi-asserted-by":"publisher","first-page":"445","DOI":"10.2217\/iim.10.33","volume":"2","author":"TB Smith","year":"2010","unstructured":"Smith, T.B.: Mri artifacts and correction strategies. Imaging Med. 2(4), 445 (2010)","journal-title":"Imaging Med."},{"key":"48_CR20","doi-asserted-by":"crossref","unstructured":"Sundaresan, V., Dinsdale, N.K.: Automated quality assessment using appearance-based simulations and hippocampus segmentation on low-field paediatric brain mr images. arXiv preprint arXiv:2410.06161 (2024)","DOI":"10.1007\/978-3-031-83008-2_4"},{"issue":"10","key":"48_CR21","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1109\/42.811270","volume":"18","author":"K Van Leemput","year":"1999","unstructured":"Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of mr images of the brain. IEEE Trans. Med. Imaging 18(10), 897\u2013908 (1999)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"48_CR22","doi-asserted-by":"crossref","unstructured":"Xiao, S., Liu, Z., Zhang, P., Muennighoff, N., Lian, D., Nie, J.Y.: C-pack: packed resources for general chinese embeddings. In: Proceedings of the 47th international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 641\u2013649 (2024)","DOI":"10.1145\/3626772.3657878"},{"key":"48_CR23","doi-asserted-by":"crossref","unstructured":"Yang, Z., et al.: All-in-one medical image restoration via task-adaptive routing. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 67\u201377. Springer (2024)","DOI":"10.1007\/978-3-031-72104-5_7"},{"key":"48_CR24","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5728\u20135739 (2022)","DOI":"10.1109\/CVPR52688.2022.00564"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04937-7_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:40:22Z","timestamp":1758260422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04937-7_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049360","9783032049377"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04937-7_48","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","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":"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":"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":"23 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":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}