{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T19:22:01Z","timestamp":1768591321268,"version":"3.49.0"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030001285","type":"print"},{"value":"9783030001292","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-00129-2_3","type":"book-chapter","created":{"date-parts":[[2018,9,11]],"date-time":"2018-09-11T16:35:39Z","timestamp":1536683739000},"page":"21-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Cardiac MR Motion Artefact Correction from K-space Using Deep Learning-Based Reconstruction"],"prefix":"10.1007","author":[{"given":"Ilkay","family":"Oksuz","sequence":"first","affiliation":[]},{"given":"James","family":"Clough","sequence":"additional","affiliation":[]},{"given":"Aurelien","family":"Bustin","sequence":"additional","affiliation":[]},{"given":"Gastao","family":"Cruz","sequence":"additional","affiliation":[]},{"given":"Claudia","family":"Prieto","sequence":"additional","affiliation":[]},{"given":"Rene","family":"Botnar","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[]},{"given":"Julia A.","family":"Schnabel","sequence":"additional","affiliation":[]},{"given":"Andrew P.","family":"King","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,9,12]]},"reference":[{"key":"3_CR1","unstructured":"Adler, J., et al.: Learning to solve inverse problems using Wasserstein loss. arXiv:1710.10898 (2017)"},{"key":"3_CR2","first-page":"1","volume":"15","author":"PF Ferreira","year":"2013","unstructured":"Ferreira, P.F., et al.: Cardiovascular magnetic resonance artefacts. JCMR 15, 1\u201341 (2013)","journal-title":"JCMR"},{"key":"3_CR3","unstructured":"Frogner, C., et al.: Learning with a Wasserstein loss. In: NIPS, pp. 2053\u20132061 (2015)"},{"key":"3_CR4","unstructured":"Han, Y., et al.: k-Space deep learning for accelerated MRI. arXiv:1805.03779 (2018)"},{"issue":"9","key":"3_CR5","first-page":"4509","volume":"26","author":"KH Jin","year":"2017","unstructured":"Jin, K.H., et al.: Deep convolutional neural network for inverse problems in imaging. IEEE TIP 26(9), 4509\u20134522 (2017)","journal-title":"IEEE TIP"},{"issue":"1","key":"3_CR6","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1002\/jmri.21214","volume":"27","author":"YC Kim","year":"2008","unstructured":"Kim, Y.C., et al.: Automatic correction of echoplanar imaging (EPI) ghosting artifacts in realtime interactive cardiac MRI using sensitivity encoding. JMRI 27(1), 239\u2013245 (2008)","journal-title":"JMRI"},{"issue":"12","key":"3_CR7","doi-asserted-by":"publisher","first-page":"6209","DOI":"10.1002\/mp.12600","volume":"44","author":"K Kwon","year":"2017","unstructured":"Kwon, K., et al.: A parallel MR imaging method using multilayer perceptron. Med. Phys. 44(12), 6209\u20136224 (2017)","journal-title":"Med. Phys."},{"issue":"10","key":"3_CR8","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1016\/j.acra.2005.07.002","volume":"12","author":"J Lotjonen","year":"2005","unstructured":"Lotjonen, J., et al.: Correction of motion artifacts from cardiac cine magnetic resonance images. Acad. Radiol. 12(10), 1273\u20131284 (2005)","journal-title":"Acad. Radiol."},{"issue":"2","key":"3_CR9","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/MSP.2007.914728","volume":"25","author":"M Lustig","year":"2008","unstructured":"Lustig, M., et al.: Compressed sensing MRI. IEEE Sig. Process. Mag. 25(2), 72\u201382 (2008)","journal-title":"IEEE Sig. Process. Mag."},{"issue":"6","key":"3_CR10","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/MSP.2017.2739299","volume":"34","author":"MT McCann","year":"2017","unstructured":"McCann, M.T., et al.: Convolutional neural networks for inverse problems in imaging: a review. IEEE Sig. Process. Mag. 34(6), 85\u201395 (2017)","journal-title":"IEEE Sig. Process. Mag."},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Oksuz, I., et al.: Deep learning using K-space based data augmentation for automated cardiac MR motion artefact detection. In: MICCAI (2018)","DOI":"10.1007\/978-3-030-00928-1_29"},{"issue":"1","key":"3_CR12","first-page":"1","volume":"18","author":"SE Petersen","year":"2016","unstructured":"Petersen, S.E., et al.: UK Biobank\u2019s cardiovascular magnetic resonance protocol. JCMR 18(1), 1\u20138 (2016)","journal-title":"JCMR"},{"issue":"4","key":"3_CR13","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1148\/rg.284065718","volume":"28","author":"F Saremi","year":"2008","unstructured":"Saremi, F., et al.: Optimizing cardiac MR imaging: practical remedies for artifacts. Radiographics 28(4), 1161\u20131187 (2008)","journal-title":"Radiographics"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Schlemper, J., et al.: A deep cascade of convolutional neural networks for MR image reconstruction. In: IPMI (2017)","DOI":"10.1007\/978-3-319-59050-9_51"},{"issue":"2","key":"3_CR15","first-page":"491","volume":"37","author":"J Schlemper","year":"2018","unstructured":"Schlemper, J., et al.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE TMI 37(2), 491\u2013503 (2018)","journal-title":"IEEE TMI"},{"issue":"4","key":"3_CR16","first-page":"600","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., et al.: Image quality assessment: from error visibility to structural similarity. IEEE TIP 13(4), 600\u2013612 (2004)","journal-title":"IEEE TIP"},{"key":"3_CR17","first-page":"1310","volume":"37","author":"G Yang","year":"2017","unstructured":"Yang, G., et al.: DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE TMI 37, 1310\u20131321 (2017)","journal-title":"IEEE TMI"},{"issue":"7697","key":"3_CR18","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1038\/nature25988","volume":"555","author":"B Zhu","year":"2018","unstructured":"Zhu, B., et al.: Image reconstruction by domain-transform manifold learning. Nature 555(7697), 487 (2018)","journal-title":"Nature"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Medical Image Reconstruction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00129-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:02:22Z","timestamp":1694390542000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-00129-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030001285","9783030001292"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00129-2_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"12 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning for Medical Image Reconstruction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmir2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmir2018\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}