{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T10:25:19Z","timestamp":1758450319510,"version":"3.44.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049469"},{"type":"electronic","value":"9783032049476"}],"license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04947-6_10","type":"book-chapter","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:32:11Z","timestamp":1758389531000},"page":"99-109","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cascaded 3D Diffusion Models for\u00a0Whole-Body 3D 18-F FDG PET\/CT Synthesis from\u00a0Demographics"],"prefix":"10.1007","author":[{"given":"Siyeop","family":"Yoon","sequence":"first","affiliation":[]},{"given":"Sifan","family":"Song","sequence":"additional","affiliation":[]},{"given":"Pengfei","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Matthew","family":"Tivnan","sequence":"additional","affiliation":[]},{"given":"Yujin","family":"Oh","sequence":"additional","affiliation":[]},{"given":"Sekeun","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Dufan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Quanzheng","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"10_CR1","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/978-3-031-53767-7_10","volume-title":"MICCAI 2023","author":"M Akrout","year":"2023","unstructured":"Akrout, M., et al.: Diffusion-based data augmentation for skin disease classification: impact across original medical datasets to fully synthetic images. In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., Yuan, Y. (eds.) MICCAI 2023. LNCS, vol. 14533, pp. 99\u2013109. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-53767-7_10"},{"issue":"3","key":"10_CR2","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/0304-4149(82)90051-5","volume":"12","author":"BD Anderson","year":"1982","unstructured":"Anderson, B.D.: Reverse-time diffusion equation models. Stoch. Proc. Appl. 12(3), 313\u2013326 (1982)","journal-title":"Stoch. Proc. Appl."},{"key":"10_CR3","first-page":"1737","volume":"202","author":"O Bar-Tal","year":"2023","unstructured":"Bar-Tal, O., Yariv, L., Lipman, Y., Dekel, T.: Multidiffusion: fusing diffusion paths for controlled image generation. Proc. Mach. Learn. Res. 202, 1737\u20131752 (2023)","journal-title":"Proc. Mach. Learn. Res."},{"key":"10_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102479","volume":"80","author":"H Chung","year":"2022","unstructured":"Chung, H., Ye, J.C.: Score-based diffusion models for accelerated MRI. Med. Image Anal. 80, 102479 (2022)","journal-title":"Med. Image Anal."},{"issue":"1","key":"10_CR5","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1038\/s41597-022-01718-3","volume":"9","author":"S Gatidis","year":"2022","unstructured":"Gatidis, S., et al.: A whole-body FDG-PET\/CT dataset with manually annotated tumor lesions. Sci. Data 9(1), 601 (2022)","journal-title":"Sci. Data"},{"issue":"11","key":"10_CR6","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"10_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/978-3-031-72986-7_8","volume-title":"ECCV 2024","author":"IE Hamamci","year":"2024","unstructured":"Hamamci, I.E., et al.: GenerateCT: text-conditional generation of 3D chest CT volumes. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) ECCV 2024. LNCS, vol. 15137, pp. 126\u2013143. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72986-7_8"},{"key":"10_CR8","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR9","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/978-3-031-16446-0_47","volume-title":"MICCAI 2022","author":"Q Hu","year":"2022","unstructured":"Hu, Q., Li, H., Zhang, J.: Domain-adaptive 3D medical image synthesis: an efficient unsupervised approach. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13436, pp. 495\u2013504. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16446-0_47"},{"key":"10_CR10","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"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Kadry, K., Gupta, S., Nezami, F.R., Edelman, E.R.: Probing the limits and capabilities of diffusion models for the anatomic editing of digital twins. npj Digit. Med. 7(1), 1\u201312 (2024)","DOI":"10.1038\/s41746-024-01332-0"},{"key":"10_CR12","first-page":"26565","volume":"35","author":"T Karras","year":"2022","unstructured":"Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. Adv. Neural. Inf. Process. Syst. 35, 26565\u201326577 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Karras, T., Aittala, M., Lehtinen, J., Hellsten, J., Aila, T., Laine, S.: Analyzing and improving the training dynamics of diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24174\u201324184 (2024)","DOI":"10.1109\/CVPR52733.2024.02282"},{"key":"10_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101938","volume":"109","author":"S Kazeminia","year":"2020","unstructured":"Kazeminia, S., et al.: GANs for medical image analysis. Artif. Intell. Med. 109, 101938 (2020)","journal-title":"Artif. Intell. Med."},{"issue":"1","key":"10_CR15","doi-asserted-by":"publisher","first-page":"7303","DOI":"10.1038\/s41598-023-34341-2","volume":"13","author":"F Khader","year":"2023","unstructured":"Khader, F., et al.: Denoising diffusion probabilistic models for 3D medical image generation. Sci. Rep. 13(1), 7303 (2023)","journal-title":"Sci. Rep."},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Kim, J., Park, H.: Adaptive latent diffusion model for 3D medical image to image translation: multi-modal magnetic resonance imaging study. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 7604\u20137613 (2024)","DOI":"10.1109\/WACV57701.2024.00743"},{"key":"10_CR17","unstructured":"Kingma, D.P., Welling, M., et\u00a0al.: Auto-encoding variational bayes (2013)"},{"key":"10_CR18","unstructured":"Lipman, Y., Chen, R.T., Ben-Hamu, H., Nickel, M., Le, M.: Flow matching for generative modeling. arXiv preprint arXiv:2210.02747 (2022)"},{"key":"10_CR19","unstructured":"Lipman, Y., et al.: Flow matching guide and code. arXiv preprint arXiv:2412.06264 (2024)"},{"issue":"4","key":"10_CR20","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1002\/mp.16847","volume":"51","author":"S Pan","year":"2024","unstructured":"Pan, S., et al.: Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model. Med. Phys. 51(4), 2538\u20132548 (2024)","journal-title":"Med. Phys."},{"issue":"1","key":"10_CR21","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1148\/radiol.2020200038","volume":"297","author":"OS Pianykh","year":"2020","unstructured":"Pianykh, O.S., et al.: Continuous learning AI in radiology: implementation principles and early applications. Radiology 297(1), 6\u201314 (2020)","journal-title":"Radiology"},{"key":"10_CR22","unstructured":"Pinaya, W.H., et\u00a0al.: Generative AI for medical imaging: extending the MONAI framework. arXiv preprint arXiv:2307.15208 (2023)"},{"key":"10_CR23","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/978-3-031-18576-2_12","volume-title":"DGM4MICCAI 2022","author":"WH Pinaya","year":"2022","unstructured":"Pinaya, W.H., et al.: Brain imaging generation with latent diffusion models. In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., Yuan, Y. (eds.) DGM4MICCAI 2022. LNCS, vol. 13609, pp. 117\u2013126. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-18576-2_12"},{"key":"10_CR24","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":"10_CR25","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1007\/978-3-031-73030-6_19","volume-title":"ECCV 2024","author":"J Schusterbauer","year":"2024","unstructured":"Schusterbauer, J., et al.: FMBoost: boosting latent diffusion with flow matching. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) ECCV 2024. LNCS, vol. 15119, pp. 338\u2013355. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-73030-6_19"},{"key":"10_CR26","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Wang, T., Yang, X.: Take CT, get pet free: AI-powered breakthrough in lung cancer diagnosis and prognosis. Cell Rep. Med. 5(4) (2024)","DOI":"10.1016\/j.xcrm.2024.101486"},{"key":"10_CR28","unstructured":"Wang, Z., et al.: Patch diffusion: faster and more data-efficient training of diffusion models. arXiv preprint arXiv:2304.12526 (2023)"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Wasserthal, J., et\u00a0al.: TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5(5), e230024 (2023)","DOI":"10.1148\/ryai.230024"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Yoon, S., et al.: High-resolution 3D CT synthesis from bidirectional x-ray images using 3d diffusion model. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp.\u00a01\u20134. IEEE (2024)","DOI":"10.1109\/ISBI56570.2024.10635648"},{"key":"10_CR31","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1007\/978-3-031-72104-5_72","volume-title":"MICCAI 2024","author":"S Yoon","year":"2024","unstructured":"Yoon, S., et al.: Volumetric conditional score-based residual diffusion model for PET\/MR denoising. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15007, pp. 754\u2013763. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72104-5_72"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04947-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:32:16Z","timestamp":1758389536000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04947-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032049469","9783032049476"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04947-6_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,21]]},"assertion":[{"value":"21 September 2025","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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}