{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:12:58Z","timestamp":1760710378860,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030597122"},{"type":"electronic","value":"9783030597139"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59713-9_14","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T05:06:21Z","timestamp":1601615181000},"page":"136-146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Learning a Gradient Guidance for Spatially Isotropic MRI Super-Resolution Reconstruction"],"prefix":"10.1007","author":[{"given":"Yao","family":"Sui","sequence":"first","affiliation":[]},{"given":"Onur","family":"Afacan","sequence":"additional","affiliation":[]},{"given":"Ali","family":"Gholipour","sequence":"additional","affiliation":[]},{"given":"Simon K.","family":"Warfield","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118633953","volume-title":"Magnetic Resonance Imaging: Physical Principles and Sequence Design","author":"RW Brown","year":"2014","unstructured":"Brown, R.W., Cheng, Y.C.N., Haacke, E.M., Thompson, M.R., Venkatesan, R.: Magnetic Resonance Imaging: Physical Principles and Sequence Design, 2nd edn. Wiley, Hoboken (2014)","edition":"2"},{"issue":"5","key":"14_CR2","doi-asserted-by":"publisher","first-page":"2139","DOI":"10.1002\/mrm.27178","volume":"80","author":"A Chaudhari","year":"2018","unstructured":"Chaudhari, A., et al.: Super-resolution musculoskeletal MRI using deep learning. Magn. Reson. Med. 80(5), 2139\u20132154 (2018)","journal-title":"Magn. Reson. Med."},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xie, Y., Zhou, Z., Shi, F., Christodoulou, A.G., Li, D.: Brain MRI super resolution using 3D deep densely connected neural networks. In: International Symposium on Biomedical Imaging, pp. 739\u2013742 (2018)","DOI":"10.1109\/ISBI.2018.8363679"},{"key":"14_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-030-00928-1_11","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Y Chen","year":"2018","unstructured":"Chen, Y., Shi, F., Christodoulou, A.G., Xie, Y., Zhou, Z., Li, D.: Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 91\u201399. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_11"},{"issue":"2","key":"14_CR5","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"14_CR6","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1109\/TMI.2010.2051680","volume":"29","author":"A Gholipour","year":"2010","unstructured":"Gholipour, A., Estroff, J.A., Warfield, S.K.: Robust super-resolution volume reconstruction from slice acquisitions: application to fetal brain MRI. IEEE Trans. Med. Imaging 29(10), 1739\u20131758 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"14_CR7","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1002\/mrm.1910340618","volume":"34","author":"H Gudbjartsson","year":"1995","unstructured":"Gudbjartsson, H., Patz, S.: The Rician distribution of noisy MRI data. Magn. Reson. Med. 34, 910\u2013914 (1995)","journal-title":"Magn. Reson. Med."},{"issue":"1","key":"14_CR8","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.bbe.2019.10.003","volume":"40","author":"J Jurek","year":"2020","unstructured":"Jurek, J., Kocinski, M., Materka, A., Elgalal, M., Majos, A.: CNN-based superresolution reconstruction of 3D MR images using thick-slice scans. Biocybern. Biomed. Eng. 40(1), 111\u2013125 (2020)","journal-title":"Biocybern. Biomed. Eng."},{"issue":"4","key":"14_CR9","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1002\/mrm.25266","volume":"73","author":"A Loktyushin","year":"2015","unstructured":"Loktyushin, A., Nickisch, H., Pohmann, R., Sch\u00f6lkopf, B.: Blind multirigid retrospective motion correction of MR images. Magn. Reson. Med. 73(4), 1457\u20131468 (2015)","journal-title":"Magn. Reson. Med."},{"key":"14_CR10","doi-asserted-by":"publisher","first-page":"170032","DOI":"10.1038\/sdata.2017.32","volume":"4","author":"F Lusebrink","year":"2017","unstructured":"Lusebrink, F., Sciarra, A., Mattern, H., Yakupov, R., Speck, O.: T1-weighted in vivo human whole brain MRI dataset with an ultrahigh isotropic resolution of 250\u00a0$$\\upmu $$m. Sci. Data 4, 170032 (2017)","journal-title":"Sci. Data"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Pham, C., Ducournau, A., Fablet, R., Rousseau, F.: Brain MRI super-resolution using deep 3D convolutional networks. In: International Symposium on Biomedical Imaging, pp. 197\u2013200 (2017)","DOI":"10.1109\/ISBI.2017.7950500"},{"key":"14_CR12","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<963::AID-MRM17>3.0.CO;2-L","volume":"42","author":"J Pipe","year":"1999","unstructured":"Pipe, J.: Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging. Magn. Reson. Med. 42, 963\u2013969 (1999)","journal-title":"Magn. Reson. Med."},{"key":"14_CR13","doi-asserted-by":"publisher","first-page":"1983","DOI":"10.1002\/mrm.24187","volume":"68","author":"E Plenge","year":"2012","unstructured":"Plenge, E., et al.: Super-resolution methods in MRI: can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time? Magn. Reson. Med. 68, 1983\u20131993 (2012)","journal-title":"Magn. Reson. Med."},{"key":"14_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/978-3-642-15705-9_75","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2010","author":"DHJ Poot","year":"2010","unstructured":"Poot, D.H.J., Van Meir, V., Sijbers, J.: General and efficient super-resolution method for multi-slice MRI. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 615\u2013622. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15705-9_75"},{"key":"14_CR15","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":"KP Pruessmann","year":"1999","unstructured":"Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P.: SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42, 952\u2013962 (1999)","journal-title":"Magn. Reson. Med."},{"issue":"5","key":"14_CR16","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1109\/TMI.2008.2007348","volume":"28","author":"RZ Shilling","year":"2009","unstructured":"Shilling, R.Z., Robbie, T.Q., Bailloeul, T., Mewes, K., Mersereau, R.M., Brummer, M.E.: A super-resolution framework for 3-D high-resolution and high-contrast imaging using 2-D multislice MRI. IEEE Trans. Med. Imaging 28(5), 633\u2013644 (2009)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"14_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-32248-9_1","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Y Sui","year":"2019","unstructured":"Sui, Y., Afacan, O., Gholipour, A., Warfield, S.K.: Isotropic MRI super-resolution reconstruction with multi-scale gradient field prior. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 3\u201311. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_1"},{"issue":"1","key":"14_CR18","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/TIP.2008.2007049","volume":"18","author":"A Thelen","year":"2009","unstructured":"Thelen, A., Frey, S., Hirsch, S., Hering, P.: Improvements in shape-from-focus for holographic reconstructions with regard to focus operators, neighborhood-size, and height value interpolation. IEEE Trans. Image Process. 18(1), 151\u2013157 (2009)","journal-title":"IEEE Trans. Image Process."},{"key":"14_CR19","unstructured":"Tsai, R.Y., Huang, T.: Multi-frame image restoration and registration. In: Advances in Computer Vision and Image Processing (1984)"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, Y., Wu, Y., Shi, J., Gee, J.: Enhanced generative adversarial network for 3D brain MRI super-resolution. In: IEEE Winter Conference on Applications of Computer Vision, pp. 3627\u20133636 (2020)","DOI":"10.1109\/WACV45572.2020.9093603"},{"issue":"1","key":"14_CR21","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error measurement to structural similarity. IEEE Trans. Image Process. 13(1), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"14_CR22","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1002\/jmri.25959","volume":"48","author":"D Zaca","year":"2018","unstructured":"Zaca, D., Hasson, U., Minati, L., Jovicich, J.: A method for retrospective estimation of natural head movement during structural MRI. J. Magn. Reson. Imaging 48(4), 927\u2013937 (2018)","journal-title":"J. Magn. Reson. Imaging"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Zhao, C., Carass, A., Dewey, B.E., Prince, J.L.: Self super-resolution for magnetic resonance images using deep networks. In: International Symposium on Biomedical Imaging, pp. 365\u2013368 (2018)","DOI":"10.1109\/ISBI.2018.8363594"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59713-9_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:04:46Z","timestamp":1759356286000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59713-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597122","9783030597139"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59713-9_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.org\/en\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1809","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":"542","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":"30% - 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":"3","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":"4","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}