{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T13:17:25Z","timestamp":1760015845278,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030781903"},{"type":"electronic","value":"9783030781910"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-78191-0_9","type":"book-chapter","created":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T06:02:29Z","timestamp":1624168949000},"page":"108-119","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MR Slice Profile Estimation by Learning to Match Internal Patch Distributions"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2033-9004","authenticated-orcid":false,"given":"Shuo","family":"Han","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8634-8128","authenticated-orcid":false,"given":"Samuel","family":"Remedios","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4939-5085","authenticated-orcid":false,"given":"Aaron","family":"Carass","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7044-9941","authenticated-orcid":false,"given":"Michael","family":"Sch\u00e4r","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6553-0876","authenticated-orcid":false,"given":"Jerry L.","family":"Prince","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,14]]},"reference":[{"unstructured":"American College of Radiology Magnetic Resonance Imaging Accreditation Program: Phantom Test guidance for use of the large MRI phantom for the ACR MRI accreditation program, p. 16 (2018). https:\/\/www.acraccreditation.org\/-\/media\/ACRAccreditation\/Documents\/MRI\/LargePhantomGuidance.pdf","key":"9_CR1"},{"unstructured":"Bell-Kligler, S., Shocher, A., Irani, M.: Blind super-resolution kernel estimation using an internal-GAN. In: Advances in Neural Information Processing Systems 32, pp. 284\u2013293. Curran Associates, Inc. (2019)","key":"9_CR2"},{"doi-asserted-by":"crossref","unstructured":"Chen, S., et al.: Unsupervised image super-resolution with an indirect supervised path. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2020)","key":"9_CR3","DOI":"10.1109\/CVPRW50498.2020.00242"},{"doi-asserted-by":"crossref","unstructured":"Cheng, Z., Gadelha, M., Maji, S., Sheldon, D.: A Bayesian perspective on the deep image prior. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2019)","key":"9_CR4","DOI":"10.1109\/CVPR.2019.00559"},{"key":"9_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/978-3-030-59722-1_16","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"S Deng","year":"2020","unstructured":"Deng, S., et al.: Isotropic reconstruction of 3D EM images with unsupervised degradation learning. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 163\u2013173. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_16"},{"unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27, pp. 2672\u20132680. Curran Associates, Inc. (2014)","key":"9_CR6"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint: 1412.6980 (2017)","key":"9_CR7"},{"doi-asserted-by":"crossref","unstructured":"LaMontagne, P.J., et al.: OASIS-3: Longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. medRxiv (2019)","key":"9_CR8","DOI":"10.1101\/2019.12.13.19014902"},{"issue":"12","key":"9_CR9","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.1088\/0031-9155\/34\/12\/016","volume":"34","author":"RA Lerski","year":"1989","unstructured":"Lerski, R.A.: An evaluation using computer simulation of two methods of slice profile determination in MRI. Phys. Med. Biol. 34(12), 1931\u20131937 (1989)","journal-title":"Phys. Med. Biol."},{"doi-asserted-by":"crossref","unstructured":"Liu, H., Michel, E., Casey, S.O., Truwit, C.L.: Actual imaging slice profile of 2D MRI. In: Medical Imaging 2002: Physics of Medical Imaging, vol. 4682, pp. 767\u2013773. SPIE (2002)","key":"9_CR10","DOI":"10.1117\/12.465627"},{"unstructured":"Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: International Conference on Learning Representations (2018)","key":"9_CR11"},{"key":"9_CR12","volume-title":"Medical Imaging Signals and Systems","author":"JL Prince","year":"2006","unstructured":"Prince, J.L., Links, J.M.: Medical Imaging Signals and Systems. Pearson Prentice Hall, Upper Saddle River (2006)"},{"doi-asserted-by":"crossref","unstructured":"Ren, D., Zhang, K., Wang, Q., Hu, Q., Zuo, W.: Neural blind deconvolution using deep priors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020)","key":"9_CR13","DOI":"10.1109\/CVPR42600.2020.00340"},{"unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2018)","key":"9_CR14"},{"key":"9_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/978-3-319-66185-8_15","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"M Weigert","year":"2017","unstructured":"Weigert, M., Royer, L., Jug, F., Myers, G.: Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 126\u2013134. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_15"},{"unstructured":"Xuan, K., et al.: Reduce slice spacing of MR images by super-resolution learned without ground-truth. arXiv preprint: arXiv:2003.12627 (2020)","key":"9_CR16"},{"unstructured":"Yaz\u0131c\u0131, Y., Foo, C., Winkler, S., Yap, K., Piliouras, G., Chandrasekhar, V.: The unusual effectiveness of averaging in GAN training. In: International Conference on Learning Representations (2019)","key":"9_CR17"},{"key":"9_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/978-3-030-00928-1_12","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"C Zhao","year":"2018","unstructured":"Zhao, C., et al.: A deep learning based anti-aliasing self super-resolution algorithm for MRI. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 100\u2013108. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_12"},{"key":"9_CR19","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.mri.2019.05.038","volume":"64","author":"C Zhao","year":"2019","unstructured":"Zhao, C., et al.: Applications of a deep learning method for anti-aliasing and super-resolution in MRI. Magn. Reson. Imaging 64, 132\u2013141 (2019)","journal-title":"Magn. Reson. Imaging"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78191-0_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T06:03:44Z","timestamp":1624169024000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78191-0_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030781903","9783030781910"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78191-0_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"14 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmi2021.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":"200","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":"59","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)"}}]}}