{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:49:17Z","timestamp":1742942957143,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322472"},{"type":"electronic","value":"9783030322489"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-32248-9_59","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"529-537","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks"],"prefix":"10.1007","author":[{"given":"Yoonmi","family":"Hong","sequence":"first","affiliation":[]},{"given":"Geng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Pew-Thian","family":"Yap","sequence":"additional","affiliation":[]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"9","key":"59_CR1","first-page":"4340","volume":"25","author":"PT Yap","year":"2016","unstructured":"Yap, P.T., Zhang, Y., Shen, D.: Multi-tissue decomposition of diffusion MRI signals via $$\\ell _ {0}$$ sparse-group estimation. IEEE Trans. Image Process. 25(9), 4340\u20134353 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"59_CR2","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.media.2016.05.008","volume":"32","author":"C Ye","year":"2016","unstructured":"Ye, C., Zhuo, J., Gullapalli, R.P., Prince, J.L.: Estimation of fiber orientations using neighborhood information. Med. Image Anal. 32, 243\u2013256 (2016)","journal-title":"Med. Image Anal."},{"issue":"7","key":"59_CR3","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1016\/j.media.2012.05.003","volume":"16","author":"B Scherrer","year":"2012","unstructured":"Scherrer, B., Gholipour, A., Warfield, S.K.: Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions. Med. Image Anal. 16(7), 1465\u20131476 (2012)","journal-title":"Med. Image Anal."},{"key":"59_CR4","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems (NIPS), pp. 3844\u20133852 (2016)"},{"key":"59_CR5","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (NIPS), pp. 2672\u20132680 (2014)"},{"key":"59_CR6","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"59_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1007\/978-3-319-66182-7_72","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"G Chen","year":"2017","unstructured":"Chen, G., Dong, B., Zhang, Y., Shen, D., Yap, P.-T.: Neighborhood matching for curved domains with application to denoising in diffusion MRI. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 629\u2013637. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_72"},{"issue":"2","key":"59_CR8","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","volume":"30","author":"DK Hammond","year":"2011","unstructured":"Hammond, D.K., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmon. Anal. 30(2), 129\u2013150 (2011)","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"59_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"59_CR10","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"},{"issue":"11","key":"59_CR11","doi-asserted-by":"publisher","first-page":"1944","DOI":"10.1109\/TPAMI.2007.1115","volume":"29","author":"IS Dhillon","year":"2007","unstructured":"Dhillon, I.S., Guan, Y., Kulis, B.: Weighted graph cuts without eigenvectors a multilevel approach. IEEE Trans. Pattern Anal. Mach. Intell. 29(11), 1944\u20131957 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"59_CR12","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1109\/TCYB.2018.2797905","volume":"49","author":"D Nie","year":"2018","unstructured":"Nie, D., Wang, L., Adeli, E., Lao, C., Lin, W., Shen, D.: 3-D fully convolutional networks for multimodal isointense infant brain image segmentation. IEEE Trans. Cybern. 49(3), 1123\u20131136 (2018)","journal-title":"IEEE Trans. Cybern."},{"key":"59_CR13","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Computer Vision and Pattern Recognition (CVPR), pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"59_CR14","unstructured":"Chen, G., Hong, Y., Huynh, K., Lin, W., Shen, D., Yap, P.T.: Prediction of multi-shell diffusion MRI data using deep neural networks with diffusion loss. In: 105th RSNA Scientific Assembly and Annual Meeting (2019)"},{"issue":"6","key":"59_CR15","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1002\/mrm.20279","volume":"52","author":"DS Tuch","year":"2004","unstructured":"Tuch, D.S.: Q-ball imaging. Magn. Reson. Med. 52(6), 1358\u20131372 (2004)","journal-title":"Magn. Reson. Med."},{"key":"59_CR16","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"},{"key":"59_CR17","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"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32248-9_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:24:55Z","timestamp":1728519895000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32248-9_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322472","9783030322489"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32248-9_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","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":"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":"13 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":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"1730","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":"539","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":"31% - 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":"3.07","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":"6.31","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"}]}}