{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T18:38:08Z","timestamp":1764700688417,"version":"3.40.3"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030008888"},{"type":"electronic","value":"9783030008895"}],"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\/978-3-030-00889-5_28","type":"book-chapter","created":{"date-parts":[[2018,9,19]],"date-time":"2018-09-19T10:26:49Z","timestamp":1537352809000},"page":"245-253","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning to Decode 7T-Like MR Image Reconstruction from 3T MR Images"],"prefix":"10.1007","author":[{"given":"Aditya","family":"Sharma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prabhjot","family":"Kaur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aditya","family":"Nigam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arnav","family":"Bhavsar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,20]]},"reference":[{"issue":"6","key":"28_CR1","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(6), 1983\u20131993 (2012). https:\/\/doi.org\/10.1002\/mrm.24187","journal-title":"Magn. Reson. Med."},{"issue":"9","key":"28_CR2","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"},{"issue":"12","key":"28_CR3","doi-asserted-by":"publisher","first-page":"2348","DOI":"10.1109\/TMI.2013.2282126","volume":"32","author":"S Roy","year":"2013","unstructured":"Roy, S., Carass, A., Prince, J.L.: Magnetic resonance image example based contrast synthesis. IEEE Trans. Med. Imaging 32(12), 2348\u20132363 (2013)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"28_CR4","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861\u20132873 (2010)","journal-title":"IEEE Trans. Image Process."},{"key":"28_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/978-3-319-46976-8_5","volume-title":"Deep Learning and Data Labeling for Medical Applications","author":"K Bahrami","year":"2016","unstructured":"Bahrami, K., Shi, F., Rekik, I., Shen, D.: Convolutional neural network for reconstruction of 7T-like images from 3T MRI using appearance and anatomical features. In: Carneiro, G., et al. (eds.) LABELS\/DLMIA -2016. LNCS, vol. 10008, pp. 39\u201347. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46976-8_5"},{"key":"28_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1007\/978-3-319-66182-7_87","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"K Bahrami","year":"2017","unstructured":"Bahrami, K., Rekik, I., Shi, F., Shen, D.: Joint reconstruction and segmentation of\u00a07T-like MR images from 3T MRI based on\u00a0cascaded convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 764\u2013772. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_87"},{"key":"28_CR7","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 \u2014 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"},{"key":"28_CR8","unstructured":"https:\/\/www.humanconnectome.org\/study\/hcp-young-adult\/document\/1200-subjects-data-release"},{"issue":"3","key":"28_CR9","doi-asserted-by":"publisher","first-page":"1975","DOI":"10.1016\/j.neuroimage.2012.05.042","volume":"62","author":"F Shi","year":"2012","unstructured":"Shi, F., Wang, L., Dai, Y., Gilmore, J.H., Lin, W., Shen, D.: LABEL: pediatric brain extraction using learning-based meta-algorithm. NeuroImage 62(3), 1975\u20131986 (2012)","journal-title":"NeuroImage"},{"key":"28_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jvcir.2015.01.007","volume":"29","author":"J Guan","year":"2015","unstructured":"Guan, J., Zhang, W., Gu, J., Ren, H.: No-reference blur assessment based on edge modeling. J. Vis. Commun. Image Represent. 29, 1\u20137 (2015)","journal-title":"J. Vis. Commun. Image Represent."}],"container-title":["Lecture Notes in Computer Science","Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00889-5_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:04:30Z","timestamp":1695081870000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-00889-5_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030008888","9783030008895"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00889-5_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"20 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DLMIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Deep Learning in Medical Image Analysis","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":"20 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dlmia2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cs.adelaide.edu.au\/~dlmia4\/","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":"85","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":"39","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":"46% - 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":"n\/a","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}