{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:10:56Z","timestamp":1780765856598,"version":"3.54.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031438974","type":"print"},{"value":"9783031438981","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\/978-3-031-43898-1_4","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:23Z","timestamp":1696115303000},"page":"36-46","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["DRMC: A Generalist Model with\u00a0Dynamic Routing for\u00a0Multi-center PET Image Synthesis"],"prefix":"10.1007","author":[{"given":"Zhiwen","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingzheng","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubo","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1016\/j.neuroimage.2018.03.045","volume":"174","author":"Y Wang","year":"2018","unstructured":"Wang, Y., et al.: 3d conditional generative adversarial networks for high-quality pet image estimation at low dose. NeuroImage 174, 550\u2013562 (2018)","journal-title":"NeuroImage"},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1016\/j.neucom.2017.06.048","volume":"267","author":"L Xiang","year":"2017","unstructured":"Xiang, L., et al.: Deep auto-context convolutional neural networks for standard-dose pet image estimation from low-dose pet\/MRI. Neurocomputing 267, 406\u2013416 (2017)","journal-title":"Neurocomputing"},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Zhou, L., Schaefferkoetter, J., Tham, I., Huang, G., Yan, J.: Supervised learning with cyclegan for low-dose FDG pet image denoising. Med. Image Anal. 65, 101770 (2020)","DOI":"10.1016\/j.media.2020.101770"},{"issue":"8","key":"4_CR4","doi-asserted-by":"publisher","first-page":"2092","DOI":"10.1109\/TMI.2022.3156614","volume":"41","author":"Y Zhou","year":"2022","unstructured":"Zhou, Y., Yang, Z., Zhang, H., Chang, E.I.C., Fan, Y., Xu, Y.: 3d segmentation guided style-based generative adversarial networks for pet synthesis. IEEE Trans. Med. Imaging 41(8), 2092\u20132104 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhou, L., Zhan, B., Fei, Y., Zhou, J., Wang, Y.: Adaptive rectification based adversarial network with spectrum constraint for high-quality pet image synthesis. Med. Image Anal. 77, 102335 (2021)","DOI":"10.1016\/j.media.2021.102335"},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1038\/s41746-021-00497-2","volume":"4","author":"A Chaudhari","year":"2021","unstructured":"Chaudhari, A., et al.: Low-count whole-body pet with deep learning in a multicenter and externally validated study. NPJ Digit. Med. 4, 127 (2021)","journal-title":"NPJ Digit. Med."},{"issue":"3","key":"4_CR7","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1109\/TRPMS.2022.3194408","volume":"7","author":"B Zhou","year":"2022","unstructured":"Zhou, B., et al.: Federated transfer learning for low-dose pet denoising: a pilot study with simulated heterogeneous data. IEEE Trans. Radiat. Plasma Med. Sci. 7(3), 284\u2013295 (2022)","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"4_CR8","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":"4_CR9","unstructured":"Jang, S.I., et al.: Spach transformer: spatial and channel-wise transformer based on local and global self-attentions for pet image denoising, September 2022"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Zeng, P., et al.: 3D CVT-GAN: a 3d convolutional vision transformer-GAN for PET reconstruction, pp. 516\u2013526, September 2022","DOI":"10.1007\/978-3-031-16446-0_49"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Guo, P., Wang, P., Zhou, J., Jiang, S., Patel, V.M.: Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2423\u20132432, June 2021","DOI":"10.1109\/CVPR46437.2021.00245"},{"key":"4_CR12","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., et al.: Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)"},{"key":"4_CR13","unstructured":"Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., Dean, J.: Outrageously large neural networks: the sparsely-gated mixture-of-experts layer, January 2017"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Wang, X., Cai, Z., Gao, D., Vasconcelos, N.: Towards universal object detection by domain attention. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7289\u20137298 (2019)","DOI":"10.1109\/CVPR.2019.00746"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Zhu, X., et al.: Uni-perceiver: pre-training unified architecture for generic perception for zero-shot and few-shot tasks. arXiv preprint arXiv:2112.01522 (2021)","DOI":"10.1109\/CVPR52688.2022.01630"},{"key":"4_CR16","unstructured":"Zhu, J., et al.: Uni-perceiver-MOE: learning sparse generalist models with conditional MOEs. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems (2022)"},{"key":"4_CR17","unstructured":"Wang, P., et al.: OFA: unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework. CoRR abs\/2202.03052 (2022)"},{"key":"4_CR18","unstructured":"Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., Finn, C.: Gradient surgery for multi-task learning. arXiv preprint arXiv:2001.06782 (2020)"},{"key":"4_CR19","unstructured":"Han, Y., Huang, G., Song, S., Yang, L., Wang, H., Wang, Y.: Dynamic neural networks: a survey, February 2021"},{"issue":"7","key":"4_CR20","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"4_CR21","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Xue, S., et al.: A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose pet. Eur. J. Nucl. Med. Mol. Imaging 49, 1619\u20137089 (2022)","DOI":"10.1007\/s00259-021-05644-1"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Charbonnier, P., Blanc-Feraud, L., Aubert, G., Barlaud, M.: Two deterministic half-quadratic regularization algorithms for computed imaging. In: Proceedings of 1st International Conference on Image Processing. vol. 2, pp. 168\u2013172 (1994)","DOI":"10.1109\/ICIP.1994.413553"},{"issue":"4","key":"4_CR24","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/42.363108","volume":"13","author":"H Hudson","year":"1994","unstructured":"Hudson, H., Larkin, R.: Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans. Med. Imaging 13(4), 601\u2013609 (1994)","journal-title":"IEEE Trans. Med. Imaging"}],"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\/978-3-031-43898-1_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T19:18:57Z","timestamp":1730229537000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43898-1_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031438974","9783031438981"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43898-1_4","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)"}}]}}