{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T01:42:52Z","timestamp":1784943772003,"version":"3.55.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439063","type":"print"},{"value":"9783031439070","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43907-0_1","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"3-12","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["PET-Diffusion: Unsupervised PET Enhancement Based on\u00a0the\u00a0Latent Diffusion Model"],"prefix":"10.1007","author":[{"given":"Caiwen","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongsheng","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mianxin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiameng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaosong","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Buades, A., Coll, B., Morel, J.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60\u201365 (2005)","DOI":"10.1109\/CVPR.2005.38"},{"key":"1_CR2","unstructured":"Chen, S., Ma, K., Zheng, Y.: Med3D: transfer learning for 3D medical image analysis. arXiv preprint arXiv:1904.00625 (2019)"},{"issue":"13","key":"1_CR3","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)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"1_CR4","first-page":"354","volume":"6064","author":"K Dabov","year":"2006","unstructured":"Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising with block-matching and 3D filtering. Image Process. Algorithms Syst. Neural Netw. Mach. Learn. 6064, 354\u2013365 (2006)","journal-title":"Image Process. Algorithms Syst. Neural Netw. Mach. Learn."},{"key":"1_CR5","unstructured":"Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"1_CR6","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840\u20136851 (2020)"},{"issue":"1","key":"1_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/2191-219X-1-23","volume":"1","author":"F Hofheinz","year":"2011","unstructured":"Hofheinz, F., et al.: Suitability of bilateral filtering for edge-preserving noise reduction in PET. EJNMMI Res. 1(1), 1\u20139 (2011)","journal-title":"EJNMMI Res."},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Jiang, C., Pan, Y., Cui, Z., Nie, D., Shen, D.: Semi-supervised standard-dose PET image generation via region-adaptive normalization and structural consistency constraint. IEEE Trans. Med. Imaging (2023)","DOI":"10.1109\/TMI.2023.3273029"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Jiang, C., Pan, Y., Cui, Z., Shen, D.: Reconstruction of standard-dose PET from low-dose PET via dual-frequency supervision and global aggregation module. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135 (2022)","DOI":"10.1109\/ISBI52829.2022.9761694"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Khader, F., et al.: Medical diffusion-denoising diffusion probabilistic models for 3D medical image generation. arXiv preprint arXiv:2211.03364 (2022)","DOI":"10.1038\/s41598-023-34341-2"},{"issue":"16","key":"1_CR11","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab3242","volume":"64","author":"W Lu","year":"2019","unstructured":"Lu, W., et al.: An investigation of quantitative accuracy for deep learning based denoising in oncological PET. Phys. Med. Biol. 64(16), 165019 (2019)","journal-title":"Phys. Med. Biol."},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Lu, Z., Li, Z., Wang, J., Shen, D.: Two-stage self-supervised cycle-consistency network for reconstruction of thin-slice MR images. arXiv preprint arXiv:2106.15395 (2021)","DOI":"10.1007\/978-3-030-87231-1_1"},{"key":"1_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1007\/978-3-030-87231-1_27","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Luo","year":"2021","unstructured":"Luo, Y., et al.: 3D transformer-GAN for high-quality PET reconstruction. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 276\u2013285. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87231-1_27"},{"key":"1_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102335","volume":"77","author":"Y Luo","year":"2022","unstructured":"Luo, Y., et al.: Adaptive rectification based adversarial network with spectrum constraint for high-quality PET image synthesis. Med. Image Anal. 77, 102335 (2022)","journal-title":"Med. Image Anal."},{"key":"1_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-319-46466-4_5","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Noroozi","year":"2016","unstructured":"Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 69\u201384. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_5"},{"key":"1_CR16","unstructured":"Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"1_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102226","volume":"74","author":"Y Onishi","year":"2021","unstructured":"Onishi, Y., et al.: Anatomical-guided attention enhances unsupervised PET image denoising performance. Med. Image Anal. 74, 102226 (2021)","journal-title":"Med. Image Anal."},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Slovis, T.L.: The ALARA concept in pediatric CT: myth or reality? Radiology 223(1), 5\u20136 (2002)","DOI":"10.1148\/radiol.2231012100"},{"issue":"21","key":"1_CR20","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac30a0","volume":"66","author":"T Song","year":"2021","unstructured":"Song, T., Yang, F., Dutta, J.: Noise2Void: unsupervised denoising of PET images. Phys. Med. Biol. 66(21), 214002 (2021)","journal-title":"Phys. Med. Biol."},{"issue":"6","key":"1_CR21","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1109\/TMI.2018.2884053","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., et al.: 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE Trans. Med. Imaging 38(6), 1328\u20131339 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"1_CR22","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1088\/0031-9155\/60\/3\/961","volume":"60","author":"J Yan","year":"2015","unstructured":"Yan, J., Lim, J., Townsend, D.: MRI-guided brain PET image filtering and partial volume correction. Phys. Med. Biol. 60(3), 961 (2015)","journal-title":"Phys. Med. Biol."},{"issue":"6","key":"1_CR23","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/s13139-020-00667-2","volume":"54","author":"S Yie","year":"2020","unstructured":"Yie, S., Kang, S., Hwang, D., Lee, J.: Self-supervised PET denoising. Nucl. Med. Mol. Imaging 54(6), 299\u2013304 (2020)","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"1_CR24","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"},{"issue":"2","key":"1_CR25","doi-asserted-by":"publisher","first-page":"285","DOI":"10.2967\/jnumed.119.230565","volume":"61","author":"X Zhang","year":"2020","unstructured":"Zhang, X., et al.: Total-body dynamic reconstruction and parametric imaging on the uEXPLORER. J. Nucl. Med. 61(2), 285\u2013291 (2020)","journal-title":"J. Nucl. Med."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43907-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T19:21:24Z","timestamp":1730229684000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43907-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439063","9783031439070"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43907-0_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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)"}}]}}