{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:21:07Z","timestamp":1776889267700,"version":"3.51.2"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721038","type":"print"},{"value":"9783031721045","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72104-5_52","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"541-550","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["PET Image Denoising Based on\u00a03D Denoising Diffusion Probabilistic Model: Evaluations on\u00a0Total-Body Datasets"],"prefix":"10.1007","author":[{"given":"Boxiao","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Savas","family":"Ozdemir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yafei","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuangyu","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuang","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"52_CR1","doi-asserted-by":"crossref","unstructured":"Barthel, H., Schroeter, M.L., Hoffmann, K.T., Sabri, O.: PET\/MR in dementia and other neurodegenerative diseases. In: Seminars in Nuclear Medicine, vol.\u00a045, pp. 224\u2013233. Elsevier (2015)","DOI":"10.1053\/j.semnuclmed.2014.12.003"},{"key":"52_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"52_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, 2780\u20132789 (2019)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"52_CR4","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: Advances in Neural Information Processing Systems, vol. 34, pp. 8780\u20138794 (2021)"},{"key":"52_CR5","doi-asserted-by":"crossref","unstructured":"Dorbala, S., Di\u00a0Carli, M.F.: Cardiac PET perfusion: prognosis, risk stratification, and clinical management. In: Seminars in Nuclear Medicine, vol.\u00a044, pp. 344\u2013357. Elsevier (2014)","DOI":"10.1053\/j.semnuclmed.2014.05.003"},{"key":"52_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102787","volume":"86","author":"Y Fu","year":"2023","unstructured":"Fu, Y., et al.: AIGAN: Attention-encoding integrated generative adversarial network for the reconstruction of low-dose CT and low-dose PET images. Med. Image Anal. 86, 102787 (2023)","journal-title":"Med. Image Anal."},{"issue":"2","key":"52_CR7","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/TRPMS.2018.2877644","volume":"3","author":"K Gong","year":"2018","unstructured":"Gong, K., Guan, J., Liu, C.C., Qi, J.: PET image denoising using a deep neural network through fine tuning. IEEE Trans. Radiat. Plasma Med. Sci. 3(2), 153\u2013161 (2018)","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"52_CR8","doi-asserted-by":"crossref","unstructured":"Gong, K., Johnson, K., El\u00a0Fakhri, G., Li, Q., Pan, T.: PET image denoising based on denoising diffusion probabilistic model. Eur. J. Nuclear Med. Mol. Imaging 1\u201311 (2023)","DOI":"10.1109\/NSSMICRTSD49126.2023.10338188"},{"key":"52_CR9","doi-asserted-by":"publisher","first-page":"96594","DOI":"10.1109\/ACCESS.2019.2929230","volume":"7","author":"F Hashimoto","year":"2019","unstructured":"Hashimoto, F., Ohba, H., Ote, K., Teramoto, A., Tsukada, H.: Dynamic PET image denoising using deep convolutional neural networks without prior training datasets. IEEE Access 7, 96594\u201396603 (2019)","journal-title":"IEEE Access"},{"key":"52_CR10","doi-asserted-by":"crossref","unstructured":"Hashimoto, F., Onishi, Y., Ote, K., Tashima, H., Reader, A.J., Yamaya, T.: Deep learning-based PET image denoising and reconstruction: a review. Radiol. Phys. Technol. 1\u201323 (2024)","DOI":"10.1007\/s12194-024-00780-3"},{"key":"52_CR11","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840\u20136851 (2020)"},{"key":"52_CR12","doi-asserted-by":"crossref","unstructured":"Jaudet, C., Weyts, K., Lechervy, A., Batalla, A., Bardet, S., Corroyer-Dulmont, A.: The impact of artificial intelligence CNN based denoising on FDG PET radiomics. Front. Oncol. 3136 (2021)","DOI":"10.3389\/fonc.2021.692973"},{"key":"52_CR13","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-031-43907-0_1","volume-title":"MICCAI 2023","author":"C Jiang","year":"2023","unstructured":"Jiang, C., et al.: PET-diffusion: unsupervised PET enhancement based on the latent diffusion model. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14220, pp. 3\u201312. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43907-0_1"},{"issue":"5","key":"52_CR14","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1007\/s10278-018-0150-3","volume":"32","author":"S Kaplan","year":"2019","unstructured":"Kaplan, S., Zhu, Y.M.: Full-dose pet image estimation from low-dose PET image using deep learning: a pilot study. J. Digit. Imaging 32(5), 773\u2013778 (2019)","journal-title":"J. Digit. Imaging"},{"key":"52_CR15","doi-asserted-by":"crossref","unstructured":"Kazerouni, A., et al.: Diffusion models for medical image analysis: a comprehensive survey. arXiv preprint arXiv:2211.07804 (2022)","DOI":"10.1016\/j.media.2023.102846"},{"key":"52_CR16","doi-asserted-by":"crossref","unstructured":"Lee, S., Chung, H., Park, M., Park, J., Ryu, W.S., Ye, J.C.: Improving 3D imaging with pre-trained perpendicular 2D diffusion models. arXiv preprint arXiv:2303.08440 (2023)","DOI":"10.1109\/ICCV51070.2023.00983"},{"issue":"21","key":"52_CR17","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab4891","volume":"64","author":"Y Lei","year":"2019","unstructured":"Lei, Y., et al.: Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks. Phys. Med. Biol. 64(21), 215017 (2019)","journal-title":"Phys. Med. Biol."},{"issue":"10","key":"52_CR18","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abfb17","volume":"66","author":"Y Lv","year":"2021","unstructured":"Lv, Y., Xi, C.: PET image reconstruction with deep progressive learning. Phys. Med. Biol. 66(10), 105016 (2021)","journal-title":"Phys. Med. Biol."},{"issue":"8","key":"52_CR19","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1001\/jamapediatrics.2013.311","volume":"167","author":"DL Miglioretti","year":"2013","unstructured":"Miglioretti, D.L., et al.: The use of computed tomography in pediatrics and the associated radiation exposure and estimated cancer risk. JAMA Pediatr. 167(8), 700\u2013707 (2013)","journal-title":"JAMA Pediatr."},{"key":"52_CR20","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.3389\/fonc.2020.01301","volume":"10","author":"Y Ming","year":"2020","unstructured":"Ming, Y., et al.: Progress and future trends in PET\/CT and PET\/MRI molecular imaging approaches for breast cancer. Front. Oncol. 10, 1301 (2020)","journal-title":"Front. Oncol."},{"key":"52_CR21","unstructured":"Nerella, S., et\u00a0al.: Transformers in healthcare: a survey. arXiv preprint arXiv:2307.00067 (2023)"},{"key":"52_CR22","unstructured":"Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning, pp. 8162\u20138171. PMLR (2021)"},{"key":"52_CR23","doi-asserted-by":"crossref","unstructured":"\u00d6zbey, M., et al.: Unsupervised medical image translation with adversarial diffusion models. IEEE Trans. Med. Imaging (2023)","DOI":"10.1109\/TMI.2023.3290149"},{"issue":"3","key":"52_CR24","doi-asserted-by":"publisher","first-page":"399","DOI":"10.2967\/jnumed.116.177592","volume":"58","author":"JD Schaefferkoetter","year":"2017","unstructured":"Schaefferkoetter, J.D., et al.: Quantitative accuracy and lesion detectability of low-dose 18F-FDG PET for lung cancer screening. J. Nucl. Med. 58(3), 399\u2013405 (2017)","journal-title":"J. Nucl. Med."},{"key":"52_CR25","unstructured":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning, pp. 2256\u20132265. PMLR (2015)"},{"key":"52_CR26","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.453","volume":"2","author":"S Van der Walt","year":"2014","unstructured":"Van der Walt, S., et al.: scikit-image: image processing in python. PeerJ 2, e453 (2014)","journal-title":"PeerJ"},{"key":"52_CR27","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"},{"issue":"4","key":"52_CR28","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":"52_CR29","unstructured":"Xie, H., et\u00a0al.: Dose-aware diffusion model for 3D ultra low-dose PET imaging. arXiv preprint arXiv:2311.04248 (2023)"},{"key":"52_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2020.164638","volume":"983","author":"H Xue","year":"2020","unstructured":"Xue, H., et al.: A 3D attention residual encoder-decoder least-square GAN for low-count PET denoising. Nucl. Instrum. Methods Phys. Res., Sect. A 983, 164638 (2020)","journal-title":"Nucl. Instrum. Methods Phys. Res., Sect. A"},{"key":"52_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101770","volume":"65","author":"L Zhou","year":"2020","unstructured":"Zhou, L., Schaefferkoetter, J.D., Tham, I.W., Huang, G., Yan, J.: Supervised learning with CycleGAN for low-dose FDG PET image denoising. Med. Image Anal. 65, 101770 (2020)","journal-title":"Med. Image Anal."},{"key":"52_CR32","doi-asserted-by":"publisher","first-page":"91336","DOI":"10.1109\/ACCESS.2020.2993493","volume":"8","author":"R Zhu","year":"2020","unstructured":"Zhu, R., Li, X., Zhang, X., Ma, M.: MRI and CT medical image fusion based on synchronized-anisotropic diffusion model. IEEE Access 8, 91336\u201391350 (2020)","journal-title":"IEEE Access"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72104-5_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T09:10:54Z","timestamp":1733562654000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72104-5_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721038","9783031721045"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72104-5_52","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","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":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}