{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T20:58:42Z","timestamp":1779397122788,"version":"3.53.1"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030338428","type":"print"},{"value":"9783030338435","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-33843-5_6","type":"book-chapter","created":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T00:13:49Z","timestamp":1571876029000},"page":"58-70","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator"],"prefix":"10.1007","author":[{"given":"Hongxiang","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matteo","family":"Figini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryutaro","family":"Tanno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano B.","family":"Blumberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enrico","family":"Kaden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Godwin","family":"Ogbole","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biobele J.","family":"Brown","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felice","family":"D\u2019Arco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David W.","family":"Carmichael","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ikeoluwa","family":"Lagunju","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helen J.","family":"Cross","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Delmiro","family":"Fernandez-Reyes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel C.","family":"Alexander","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,10,24]]},"reference":[{"issue":"3","key":"6_CR1","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1002\/1522-2594(200103)45:3<470::AID-MRM1062>3.0.CO;2-E","volume":"45","author":"YZ Wadghiri","year":"2001","unstructured":"Wadghiri, Y.Z., Johnson, G., Turnbull, D.H.: Sensitivity and performance time in MRI dephasing artifact reduction methods. Magn. Reson. Med. 45(3), 470\u2013476 (2001)","journal-title":"Magn. Reson. Med."},{"key":"6_CR2","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.neuroimage.2013.05.057","volume":"80","author":"SN Sotiropoulos","year":"2013","unstructured":"Sotiropoulos, S.N., et al.: Advances in diffusion MRI acquisition and processing in the human connectome project. NeuroImage 80, 125\u2013143 (2013)","journal-title":"NeuroImage"},{"issue":"6","key":"6_CR3","doi-asserted-by":"publisher","first-page":"1528","DOI":"10.1002\/jmri.26637","volume":"49","author":"JP Marques","year":"2019","unstructured":"Marques, J.P., Simonis, F.F.J., Webb, A.G.: Low-field MRI: an MR physics perspective. J. Magn. Reson. Imaging 49(6), 1528\u20131542 (2019)","journal-title":"J. Magn. Reson. Imaging"},{"key":"6_CR4","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":"9","key":"6_CR5","doi-asserted-by":"publisher","first-page":"2085","DOI":"10.1109\/TMI.2016.2549918","volume":"35","author":"K Bahrami","year":"2016","unstructured":"Bahrami, K., Shi, F., Zong, X., Shin, H.W., An, H., Shen, D.: Reconstruction of 7T-Like Images from 3T MRI. IEEE Trans. Med. Imaging 35(9), 2085\u20132097 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-319-68127-6_2","volume-title":"Simulation and Synthesis in Medical Imaging","author":"JM Wolterink","year":"2017","unstructured":"Wolterink, J.M., Dinkla, A.M., Savenije, M.H.F., Seevinck, P.R., van den Berg, C.A.T., I\u0161gum, I.: Deep MR to CT synthesis using unpaired data. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2017. LNCS, vol. 10557, pp. 14\u201323. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68127-6_2"},{"key":"6_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/978-3-030-00928-1_60","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"JP Cohen","year":"2018","unstructured":"Cohen, J.P., Luck, M., Honari, S.: Distribution matching losses can hallucinate features in medical image translation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 529\u2013536. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_60"},{"key":"6_CR8","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.neuroimage.2017.02.089","volume":"152","author":"DC Alexander","year":"2017","unstructured":"Alexander, D.C., et al.: Image quality transfer and applications in diffusion MRI. NeuroImage 152, 283\u2013298 (2017)","journal-title":"NeuroImage"},{"key":"6_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/978-3-319-46723-8_31","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"R Tanno","year":"2016","unstructured":"Tanno, R., Ghosh, A., Grussu, F., Kaden, E., Criminisi, A., Alexander, D.C.: Bayesian image quality transfer. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 265\u2013273. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_31"},{"key":"6_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/978-3-319-66182-7_70","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"R Tanno","year":"2017","unstructured":"Tanno, R., Worrall, D.E., Ghosh, A., Kaden, E., Sotiropoulos, S.N., Criminisi, A., Alexander, D.C.: Bayesian image quality transfer with CNNs: exploring uncertainty in dMRI super-resolution. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 611\u2013619. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_70"},{"key":"6_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/978-3-030-00928-1_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"SB Blumberg","year":"2018","unstructured":"Blumberg, S.B., Tanno, R., Kokkinos, I., Alexander, D.C.: Deeper image quality transfer: training low-memory neural networks for 3D images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 118\u2013125. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_14"},{"key":"6_CR12","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1016\/j.neuroimage.2005.02.018","volume":"26","author":"J Ashburner","year":"2005","unstructured":"Ashburner, J., Friston, K.J.: Unified segmentation. NeuroImage 26, 839\u2013851 (2005)","journal-title":"NeuroImage"},{"key":"6_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/978-3-319-66185-8_16","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"L Heinrich","year":"2017","unstructured":"Heinrich, L., Bogovic, J.A., Saalfeld, S.: Deep learning for isotropic super-resolution from non-isotropic 3D electron microscopy. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 135\u2013143. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_16"},{"key":"6_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"6_CR15","first-page":"770","volume":"2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CVPR 2016, 770\u2013778 (2016)","journal-title":"CVPR"},{"key":"6_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-319-46475-6_25","volume-title":"Computer Vision \u2013 ECCV 2016","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391\u2013407. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_25"},{"key":"6_CR17","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1016\/j.nicl.2017.12.022","volume":"17","author":"R Guerrero","year":"2018","unstructured":"Guerrero, R., et al.: White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks. NeuroImage Clin. 17, 918\u2013934 (2018)","journal-title":"NeuroImage Clin."},{"issue":"4","key":"6_CR18","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 visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR19","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"key":"6_CR20","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"6_CR21","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS 2010, PMLR, vol. 9, pp. 249\u2013256 (2010)"}],"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\/978-3-030-33843-5_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T23:03:07Z","timestamp":1729724587000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33843-5_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030338428","9783030338435"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33843-5_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"24 October 2019","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":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmir2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmir-2019\/home","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","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":"24","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":"75% - 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":"3","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)"}}]}}