{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:47:24Z","timestamp":1780930044101,"version":"3.54.1"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031172465","type":"print"},{"value":"9783031172472","type":"electronic"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-17247-2_8","type":"book-chapter","created":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T23:35:39Z","timestamp":1663803339000},"page":"75-83","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Noise-Level-Aware Framework for PET Image Denoising"],"prefix":"10.1007","author":[{"given":"Ye","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianan","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodong","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott","family":"Wollenweber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Floris","family":"Jansen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Se-In","family":"Jang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyungsang","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuang","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quanzheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"issue":"2","key":"8_CR1","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/TRPMS.2018.2877644","volume":"3","author":"K Gong","year":"2019","unstructured":"Gong, K., et al.: PET image denoising using a deep neural network through fine tuning. IEEE Trans. Radiat. Plasma Med. Sci. 3(2), 153\u2013161 (2019)","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"issue":"12","key":"8_CR2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0081390","volume":"8","author":"J Dutta","year":"2013","unstructured":"Dutta, J., Leahy, R.M., Li, Q.: Non-local means denoising of dynamic PET images. PLoS ONE 8(12), e81390 (2013)","journal-title":"PLoS ONE"},{"issue":"3","key":"8_CR3","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1109\/TMI.2013.2292881","volume":"33","author":"C Chan","year":"2014","unstructured":"Chan, C., et al.: Postreconstruction nonlocal means filtering of whole-body PET with an anatomical prior. IEEE Trans. Med. Imaging 33(3), 636\u2013650 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"13","key":"8_CR4","doi-asserted-by":"publisher","first-page":"2780","DOI":"10.1007\/s00259-019-04468-4","volume":"46","author":"J Cui","year":"2019","unstructured":"Cui, J., et al.: PET image denoising using unsupervised deep learning. Eur. J. Nucl. Med. Mol. Imaging 46(13), 2780\u20132789 (2019). https:\/\/doi.org\/10.1007\/s00259-019-04468-4","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"8","key":"8_CR5","doi-asserted-by":"publisher","first-page":"3555","DOI":"10.1002\/mp.13626","volume":"46","author":"JH Ouyang","year":"2019","unstructured":"Ouyang, J.H., et al.: Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss. Med. Phys. 46(8), 3555\u20133564 (2019)","journal-title":"Med. Phys."},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Cui, J.A., et al.: Populational and individual information based PET image denoising using conditional unsupervised learning. Phys. Med. Biol. 66(15) (2021)","DOI":"10.1088\/1361-6560\/ac108e"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Zhou, L., et al.: Supervised learning with cyclegan for low-dose FDG PET image denoising. Med. Image Anal. 65 (2020)","DOI":"10.1016\/j.media.2020.101770"},{"issue":"11","key":"8_CR8","doi-asserted-by":"publisher","first-page":"3154","DOI":"10.1109\/TMI.2021.3076191","volume":"40","author":"B Zhou","year":"2021","unstructured":"Zhou, B., et al.: MDPET: a unified motion correction and denoising adversarial network for low-dose gated PET. IEEE Trans. Med. Imaging 40(11), 3154\u20133164 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Song, T.A., Yang, F., Dutta, J.: Noise2Void: unsupervised denoising of PET images. Phys. Med. Biol. 66(21) (2021)","DOI":"10.1088\/1361-6560\/ac30a0"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Onishi, Y., et al., Anatomical-guided attention enhances unsupervised PET image denoising performance. Med. Image Anal. 74 (2021)","DOI":"10.1016\/j.media.2021.102226"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: Multi-Stage Progressive Image Restoration. in CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01458"},{"issue":"14","key":"8_CR12","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aacbf0","volume":"63","author":"Y Li","year":"2018","unstructured":"Li, Y., et al.: A projection image database to investigate factors affecting image quality in weight-based dosing: application to pediatric renal SPECT. Phys. Med. Biol. 63(14), 145004 (2018)","journal-title":"Phys. Med. Biol."},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man Cybern. 9(1) (1979)","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"8_CR14","unstructured":"Ba, J., Kingma, D.P.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014)"},{"key":"8_CR15","unstructured":"Hutter, F., Loshchilov, I.: SGDR: Stochastic gradient descent with warm restarts, in ICLR (2017)"}],"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\/978-3-031-17247-2_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T23:45:38Z","timestamp":1663803938000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17247-2_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031172465","9783031172472"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17247-2_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"22 September 2022","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":"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":"22 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":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmir2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmir2022\/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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","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":"15","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":"79% - 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":"2,43","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":"1,58","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)"}}]}}