{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:50:01Z","timestamp":1743083401207,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164453"},{"type":"electronic","value":"9783031164460"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16446-0_55","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:02:47Z","timestamp":1663318967000},"page":"582-592","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Invertible Sharpening Network for\u00a0MRI Reconstruction Enhancement"],"prefix":"10.1007","author":[{"given":"Siyuan","family":"Dong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric Z.","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yikang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Terrence","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanhui","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"issue":"48","key":"55_CR1","doi-asserted-by":"publisher","first-page":"30088","DOI":"10.1073\/pnas.1907377117","volume":"117","author":"V Antun","year":"2020","unstructured":"Antun, V., Renna, F., Poon, C., Adcock, B., Hansen, A.C.: On instabilities of deep learning in image reconstruction and the potential costs of AI. Proc. Natl. Acad. Sci. 117(48), 30088\u201330095 (2020)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"55_CR2","unstructured":"Behrmann, J., Grathwohl, W., Chen, R.T., Duvenaud, D., Jacobsen, J.H.: Invertible residual networks. In: International Conference on Machine Learning, pp. 573\u2013582. PMLR (2019)"},{"key":"55_CR3","doi-asserted-by":"crossref","unstructured":"Blau, Y., Michaeli, T.: The perception-distortion tradeoff. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6228\u20136237 (2018)","DOI":"10.1109\/CVPR.2018.00652"},{"key":"55_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/978-3-030-59713-9_9","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"EZ Chen","year":"2020","unstructured":"Chen, E.Z., Chen, T., Sun, S.: MRI image reconstruction via learning optimization using neural ODEs. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 83\u201393. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_9"},{"key":"55_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1007\/978-3-030-32251-9_78","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Duan","year":"2019","unstructured":"Duan, J., et al.: VS-Net: variable splitting network for accelerated parallel MRI reconstruction. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 713\u2013722. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_78"},{"key":"55_CR6","doi-asserted-by":"crossref","unstructured":"Jun, Y., Shin, H., Eo, T., Hwang, D.: Joint deep model-based MR image and coil sensitivity reconstruction network (Joint-ICNet) for fast MRI. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5270\u20135279 (2021)","DOI":"10.1109\/CVPR46437.2021.00523"},{"key":"55_CR7","first-page":"1","volume":"31","author":"DP Kingma","year":"2018","unstructured":"Kingma, D.P., Dhariwal, P.: Glow: Generative flow with invertible 1 $$\\times $$ 1 convolutions. Adv. Neural Inf. Process. Syst. 31, 1\u201311 (2018)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"55_CR8","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1109\/MSP.2019.2950640","volume":"37","author":"F Knoll","year":"2020","unstructured":"Knoll, F., et al.: Deep-learning methods for parallel magnetic resonance imaging reconstruction: a survey of the current approaches, trends, and issues. IEEE Signal Process. Mag. 37(1), 128\u2013140 (2020)","journal-title":"IEEE Signal Process. Mag."},{"issue":"6","key":"55_CR9","doi-asserted-by":"publisher","first-page":"3054","DOI":"10.1002\/mrm.28338","volume":"84","author":"F Knoll","year":"2020","unstructured":"Knoll, F., et al.: Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 FastMRI challenge. Magn. Reson. Med. 84(6), 3054\u20133070 (2020)","journal-title":"Magn. Reson. Med."},{"issue":"1","key":"55_CR10","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190007","volume":"2","author":"F Knoll","year":"2020","unstructured":"Knoll, F., et al.: fastMRI: a publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning. Radiol. Artif. Intell. 2(1), e190007 (2020)","journal-title":"Radiol. Artif. Intell."},{"key":"55_CR11","unstructured":"Li, W., et al.: Best-buddy GANs for highly detailed image super-resolution. arXiv preprint arXiv:2103.15295 (2021)"},{"key":"55_CR12","unstructured":"Malkiel, I., Ahn, S., Taviani, V., Menini, A., Wolf, L., Hardy, C.J.: Conditional WGANs with adaptive gradient balancing for sparse MRI reconstruction. arXiv preprint arXiv:1905.00985 (2019)"},{"key":"55_CR13","doi-asserted-by":"crossref","unstructured":"Menon, S., Damian, A., Hu, S., Ravi, N., Rudin, C.: Pulse: self-supervised photo upsampling via latent space exploration of generative models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2437\u20132445 (2020)","DOI":"10.1109\/CVPR42600.2020.00251"},{"issue":"9","key":"55_CR14","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1109\/TMI.2021.3075856","volume":"40","author":"MJ Muckley","year":"2021","unstructured":"Muckley, M.J., et al.: Results of the 2020 fastMRI challenge for machine learning MR image reconstruction. IEEE Trans. Med. Imaging 40(9), 2306\u20132317 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"55_CR15","doi-asserted-by":"publisher","first-page":"204825","DOI":"10.1109\/ACCESS.2020.3034287","volume":"8","author":"N Pezzotti","year":"2020","unstructured":"Pezzotti, N., et al.: An adaptive intelligence algorithm for undersampled knee MRI reconstruction. IEEE Access 8, 204825\u2013204838 (2020)","journal-title":"IEEE Access"},{"issue":"6","key":"55_CR16","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TMI.2018.2820120","volume":"37","author":"TM Quan","year":"2018","unstructured":"Quan, T.M., Nguyen-Duc, T., Jeong, W.K.: Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans. Med. Imaging 37(6), 1488\u20131497 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"55_CR17","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":"2","key":"55_CR18","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TMI.2017.2760978","volume":"37","author":"J Schlemper","year":"2017","unstructured":"Schlemper, J., Caballero, J., Hajnal, J.V., Price, A.N., Rueckert, D.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imaging 37(2), 491\u2013503 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"55_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1007\/978-3-030-00928-1_27","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"M Seitzer","year":"2018","unstructured":"Seitzer, M., et al.: Adversarial and perceptual refinement for compressed sensing MRI reconstruction. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 232\u2013240. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_27"},{"key":"55_CR20","unstructured":"S\u00f8nderby, C.K., Caballero, J., Theis, L., Shi, W., Husz\u00e1r, F.: Amortised map inference for image super-resolution. arXiv preprint arXiv:1610.04490 (2016)"},{"key":"55_CR21","doi-asserted-by":"crossref","unstructured":"Sriram, A., Zbontar, J., Murrell, T., Zitnick, C.L., Defazio, A., Sodickson, D.K.: Grappanet: combining parallel imaging with deep learning for multi-coil MRI reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14315\u201314322 (2020)","DOI":"10.1109\/CVPR42600.2020.01432"},{"key":"55_CR22","unstructured":"Wang, P., Chen, E.Z., Chen, T., Patel, V.M., Sun, S.: Pyramid convolutional RNN for MRI reconstruction. arXiv preprint arXiv:1912.00543 (2019)"},{"issue":"4","key":"55_CR23","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":"55_CR24","unstructured":"Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003. vol. 2, pp. 1398\u20131402. IEEE (2003)"},{"key":"55_CR25","doi-asserted-by":"crossref","unstructured":"Yang, G., Lv, J., Chen, Y., Huang, J., Zhu, J.: Generative adversarial networks (GAN) powered fast magnetic resonance imaging-mini review, comparison and perspectives. arXiv preprint arXiv:2105.01800 (2021)","DOI":"10.1007\/978-3-030-91390-8_13"},{"key":"55_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16446-0_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T07:10:00Z","timestamp":1721373000000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16446-0_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164453","9783031164460"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16446-0_55","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":"17 September 2022","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":"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":"18 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":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2022\/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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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","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":"5","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)"}}]}}