{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T20:49:29Z","timestamp":1758401369948,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031212055"},{"type":"electronic","value":"9783031212062"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21206-2_4","type":"book-chapter","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T01:02:53Z","timestamp":1669856573000},"page":"38-49","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Correction of\u00a0Susceptibility Distortion in\u00a0EPI: A Semi-supervised Approach with\u00a0Deep Learning"],"prefix":"10.1007","author":[{"given":"Antoine","family":"Legouhy","sequence":"first","affiliation":[]},{"given":"Mark","family":"Graham","sequence":"additional","affiliation":[]},{"given":"Michele","family":"Guerreri","sequence":"additional","affiliation":[]},{"given":"Whitney","family":"Stee","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Villemonteix","sequence":"additional","affiliation":[]},{"given":"Philippe","family":"Peigneux","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Graham, M.S., Drobnjak, I., Jenkinson, M., Zhang, H.: Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI. PloS One 12(10), e0185647 (2017)","DOI":"10.1371\/journal.pone.0185647"},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"76","DOI":"10.3389\/fninf.2019.00076","volume":"13","author":"X Gu","year":"2019","unstructured":"Gu, X., Eklund, A.: Evaluation of six phase encoding based susceptibility distortion correction methods for diffusion MRI. Front. Neuroinform. 13, 76 (2019)","journal-title":"Front. Neuroinform."},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Schilling, K.G., et al.: Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps. PLoS One 15(7), e0236418 (2020)","DOI":"10.1371\/journal.pone.0236418"},{"issue":"2","key":"4_CR4","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1016\/S1053-8119(03)00336-7","volume":"20","author":"JL Andersson","year":"2003","unstructured":"Andersson, J.L., Skare, S., Ashburner, J.: How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20(2), 870\u2013888 (2003)","journal-title":"Neuroimage"},{"issue":"5","key":"4_CR5","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1109\/TMI.2021.3134496","volume":"41","author":"Y Qiao","year":"2022","unstructured":"Qiao, Y., Shi, Y.: Unsupervised deep learning for FOD-based susceptibility distortion correction in diffusion MRI. IEEE Trans. Med. Imaging 41(5), 1165\u20131175 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.mri.2022.05.016","volume":"92","author":"JP Begnoche","year":"2022","unstructured":"Begnoche, J.P., Schilling, K.G., Boyd, B.D., Cai, L.Y., Taylor, W.D., Landman, B.A.: EPI susceptibility correction introduces significant differences far from local areas of high distortion. Magn. Reson. Imaging 92, 1\u20139 (2022)","journal-title":"Magn. Reson. Imaging"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Zahneisen, B., Baeumler, K., Zaharchuk, G., Fleischmann, D., Zeineh, M.: Deep flow-net for EPI distortion estimation. Neuroimage 217, 116886 (2020)","DOI":"10.1016\/j.neuroimage.2020.116886"},{"key":"4_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.mri.2020.04.004","volume":"71","author":"STM Duong","year":"2020","unstructured":"Duong, S.T.M., Phung, S.L., Bouzerdoum, A., Schira, M.M.: An unsupervised deep learning technique for susceptibility artifact correction in reversed phase-encoding EPI images. Magn. Reson. Imaging 71, 1\u201310 (2020)","journal-title":"Magn. Reson. Imaging"},{"issue":"2","key":"4_CR9","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/TMI.2011.2163944","volume":"31","author":"T Rohlfing","year":"2012","unstructured":"Rohlfing, T.: Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable. IEEE Trans. Med. Imaging 31(2), 153\u2013163 (2012)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"4_CR10","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1002\/mrm.20939","volume":"56","author":"I Drobnjak","year":"2006","unstructured":"Drobnjak, I., Gavaghan, D., S\u00fcli, E., Pitt-Francis, J., Jenkinson, M.: Development of a functional magnetic resonance imaging simulator for modeling realistic rigid-body motion artifacts. Magn. Reson. Med. 56(2), 364\u2013380 (2006)","journal-title":"Magn. Reson. Med."},{"issue":"1","key":"4_CR11","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1002\/mrm.1910340111","volume":"34","author":"P Jezzard","year":"1995","unstructured":"Jezzard, P., Balaban, R.S.: Correction for geometric distortion in echo planar images from B0 field variations. Magn. Reson. Med. 34(1), 65\u201373 (1995)","journal-title":"Magn. Reson. Med."},{"issue":"4","key":"4_CR12","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1002\/jmri.20032","volume":"19","author":"PS Morgan","year":"2004","unstructured":"Morgan, P.S., Bowtell, R.W., McIntyre, D.J., Worthington, B.S.: Correction of spatial distortion in EPI due to inhomogeneous static magnetic fields using the reversed gradient method. J. Magn. Reson. Imaging 19(4), 499\u2013507 (2004)","journal-title":"J. Magn. Reson. Imaging"},{"issue":"3","key":"4_CR13","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1109\/42.158935","volume":"11","author":"H Chang","year":"1992","unstructured":"Chang, H., Fitzpatrick, J.M.: A technique for accurate magnetic resonance imaging in the presence of field inhomogeneities. IEEE Trans. Med. Imaging 11(3), 319\u201329 (1992)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Fu Y., Lei Y., Wang T., Curran W.J., Liu T., Yang X.: Deep learning in medical image registration: a review. Phys. Med. Biol. 65, 20TR01 (2020)","DOI":"10.1088\/1361-6560\/ab843e"},{"key":"4_CR15","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/s00138-020-01060-x","volume":"31","author":"G Haskins","year":"2020","unstructured":"Haskins, G., Kruger, U., Yan, P.: Deep learning in medical image registration: a survey. Mach. Vis. Appl. 31, 8 (2020)","journal-title":"Mach. Vis. Appl."},{"key":"4_CR16","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J.V., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38, 1788\u20131800 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR17","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.media.2019.07.006","volume":"57","author":"AV Dalca","year":"2019","unstructured":"Dalca, A.V., Balakrishnan, G., Guttag, J.V., Sabuncu, M.R.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med. Image Anal. 57, 226\u2013236 (2019)","journal-title":"Med. Image Anal."},{"key":"4_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"}],"container-title":["Lecture Notes in Computer Science","Computational Diffusion MRI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21206-2_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T01:04:19Z","timestamp":1669856659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21206-2_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031212055","9783031212062"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21206-2_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CDMRI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Computational Diffusion MRI","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cdmri2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cmic.cs.ucl.ac.uk\/cdmri\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"92% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}