{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:48:25Z","timestamp":1758268105043,"version":"3.44.0"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049643","type":"print"},{"value":"9783032049650","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"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\/978-3-032-04965-0_59","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T08:07:12Z","timestamp":1758182832000},"page":"627-637","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with\u00a0Enhanced Efficiency"],"prefix":"10.1007","author":[{"given":"Minye","family":"Shao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingyu","family":"Miao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingkun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yawen","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Long","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yefeng","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"59_CR1","doi-asserted-by":"crossref","unstructured":"Antonelli, M., Reinke, A., Bakas, S.E.A.: The medical segmentation decathlon. Nat. Commun. 13(1), 4128 (2022)","DOI":"10.1038\/s41467-022-30695-9"},{"key":"59_CR2","doi-asserted-by":"crossref","unstructured":"Butte, S., Wang, H., Xian, M.E.A.: Sharp-GAN: Sharpness loss regularized GAN for histopathology image synthesis. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, pp.\u00a01\u20135 (2022)","DOI":"10.1109\/ISBI52829.2022.9761534"},{"key":"59_CR3","doi-asserted-by":"crossref","unstructured":"Cao, S., Konz, N., Duncan, J.E.A.: deep learning for breast MRI style transfer with limited training data. J. Digit. Imaging 36(2), 666\u2013678 (2023)","DOI":"10.1007\/s10278-022-00755-z"},{"key":"59_CR4","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I.E.A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"59_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yang, X., Yue, X.E.A.: A general variation-driven network for medical image synthesis. Appl. Intell. 54(4), 3295\u20133307 (2024)","DOI":"10.1007\/s10489-023-05017-1"},{"key":"59_CR6","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M.E.A.: Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8789\u20138797 (2018)","DOI":"10.1109\/CVPR.2018.00916"},{"key":"59_CR7","doi-asserted-by":"crossref","unstructured":"Fang, Y., Zhu, H., Zeng, Y.E.A.: Perceptual quality assessment of smartphone photography. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3677\u20133686 (2020)","DOI":"10.1109\/CVPR42600.2020.00373"},{"key":"59_CR8","unstructured":"Hamamci, I.E., Er, S., Almas, F.E.A.: A foundation model utilizing chest CT volumes and radiology reports for supervised-level zero-shot detection of abnormalities. arXiv preprint arXiv:2403.17834 (2024)"},{"key":"59_CR9","doi-asserted-by":"crossref","unstructured":"Hamamci, I.E., Er, S., Sekuboyina, A.E.A.: Generatect: text-conditional generation of 3D chest CT volumes. arXiv preprint arXiv:2305.16037 (2023)","DOI":"10.1007\/978-3-031-72986-7_8"},{"key":"59_CR10","doi-asserted-by":"crossref","unstructured":"Han, K., Xiong, Y., You, C.E.A.: Medgen3d: a deep generative framework for paired 3D image and mask generation. In: Proceedings of the MICCAI, pp. 759\u2013769 (2023)","DOI":"10.1007\/978-3-031-43907-0_72"},{"key":"59_CR11","unstructured":"He, Y., Guo, P., Tang, Y.E.A.: Vista3d: versatile imaging segmentation and annotation model for 3D computed tomography. arXiv preprint arXiv:2406.05285 (2024)"},{"key":"59_CR12","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T.E.A.: Gans trained by a two time-scale update rule converge to a local NASH equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"59_CR13","unstructured":"Ho, J., Chan, W., Saharia, C.E.A.: Imagen video: high definition video generation with diffusion models. arXiv preprint arXiv:2210.02303 (2022)"},{"key":"59_CR14","doi-asserted-by":"crossref","unstructured":"Hou, L., Agarwal, A., Samaras, D.E.A.: Robust histopathology image analysis: to label or to synthesize? In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8533\u20138542 (2019)","DOI":"10.1109\/CVPR.2019.00873"},{"key":"59_CR15","doi-asserted-by":"crossref","unstructured":"Huang, Z., et\u00a0al.: Vbench: Comprehensive benchmark suite for video generative models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21807\u201321818 (2024)","DOI":"10.1109\/CVPR52733.2024.02060"},{"key":"59_CR16","doi-asserted-by":"crossref","unstructured":"Kazerouni, A., Aghdam, E.K., Heidari, M.E.A.: Diffusion models in medical imaging: a comprehensive survey. Med. Image Anal. 88, 102846 (2023)","DOI":"10.1016\/j.media.2023.102846"},{"key":"59_CR17","doi-asserted-by":"crossref","unstructured":"Ke, J., Wang, Q., Wang, Y.E.A.: Musiq: multi-scale image quality transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5148\u20135157 (2021)","DOI":"10.1109\/ICCV48922.2021.00510"},{"key":"59_CR18","doi-asserted-by":"crossref","unstructured":"Konz, N., Chen, Y., Dong, H.E.A.: Anatomically-controllable medical image generation with segmentation-guided diffusion models. In: Proceedings of the MICCAI, pp. 88\u201398 (2024)","DOI":"10.1007\/978-3-031-72104-5_9"},{"key":"59_CR19","doi-asserted-by":"crossref","unstructured":"Lalande, A., Chen, Z., Pommier, T.E.A.: Deep learning methods for automatic evaluation of delayed enhancement-MRI. the results of the EMIDEC challenge. Med. Image Anal. 79, 102428 (2022)","DOI":"10.1016\/j.media.2022.102428"},{"key":"59_CR20","doi-asserted-by":"crossref","unstructured":"Lamba, R., McGahan, J.P., Corwin, M.T.E.A.: Ct hounsfield numbers of soft tissues on unenhanced abdominal CT scans: variability between two different manufacturers\u2019 MDCT scanners. Am. J. Roentgenol. 203(5), 1013\u20131020 (2014)","DOI":"10.2214\/AJR.12.10037"},{"key":"59_CR21","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhu, Z.L., Han, L.H.E.A.: Amt: All-pairs multi-field transforms for efficient frame interpolation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9801\u20139810 (2023)","DOI":"10.1109\/CVPR52729.2023.00945"},{"key":"59_CR22","unstructured":"Loshchilov, I.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"59_CR23","doi-asserted-by":"crossref","unstructured":"Moser, J., Sheard, S., Edyvean, S.E.A.: Radiation dose-reduction strategies in thoracic CT. Clin. Radiol. 72(5), 407\u2013420 (2017)","DOI":"10.1016\/j.crad.2016.11.021"},{"key":"59_CR24","doi-asserted-by":"crossref","unstructured":"Pan, M., Gan, Y., Zhou, F.E.A.: Diffuseir: diffusion models for isotropic reconstruction of 3D microscopic images. In: Proceedings of the MICCAI, pp. 323\u2013332 (2023)","DOI":"10.1007\/978-3-031-43999-5_31"},{"key":"59_CR25","unstructured":"Radford, A., Kim, J.W., Hallacy, C.E.A.: Learning transferable visual models from natural language supervision. In: Proceedings of the International Conference on Machine Learning, pp. 8748\u20138763 (2021)"},{"key":"59_CR26","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D.E.A.: 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":"59_CR27","unstructured":"Saharia, C., Chan, W., Saxena, S.E.A.: Photorealistic text-to-image diffusion models with deep language understanding. In: Advances in Neural Information Processing Systems, vol. 35, pp. 36479\u201336494 (2022)"},{"key":"59_CR28","doi-asserted-by":"crossref","unstructured":"Shao, M., et al.: Rethinking brain tumor segmentation from the frequency domain perspective. IEEE Trans. Med. Imaging (2025)","DOI":"10.1109\/TMI.2025.3579213"},{"key":"59_CR29","doi-asserted-by":"crossref","unstructured":"Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15, 1\u201328 (2015)","DOI":"10.1186\/s12880-015-0068-x"},{"key":"59_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1007\/978-3-030-58536-5_24","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Teed","year":"2020","unstructured":"Teed, Z., Deng, J.: RAFT: Recurrent All-pairs Field Transforms for Optical Flow. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 402\u2013419. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_24"},{"key":"59_CR31","unstructured":"Unterthiner, T., van Steenkiste, S., Kurach, K.E.A.: FVD: a new metric for video generation (2019)"},{"key":"59_CR32","unstructured":"Villegas, R., Babaeizadeh, M., Kindermans, P.J.E.A.: Phenaki: variable length video generation from open domain textual descriptions. In: Proceedings of the International Conference on Learning Representations (2022)"},{"key":"59_CR33","unstructured":"Wang, Y., He, Y., Li, Y.E.A.: Internvid: a large-scale video-text dataset for multimodal understanding and generation. arXiv preprint arXiv:2307.06942 (2023)"},{"key":"59_CR34","doi-asserted-by":"crossref","unstructured":"Xu, Y., Sun, L., Peng, W.E.A.: Medsyn: text-guided anatomy-aware synthesis of high-fidelity 3D CT images. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3415032"},{"key":"59_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/978-3-030-32245-8_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Yang","year":"2019","unstructured":"Yang, J., Dvornek, N.C., Zhang, F., Chapiro, J., Lin, M.D., Duncan, J.S.: Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-modality Liver Segmentation. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 255\u2013263. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_29"},{"key":"59_CR36","doi-asserted-by":"crossref","unstructured":"Yu, X., Li, G., Lou, W.E.A.: Diffusion-based data augmentation for nuclei image segmentation. In: Proceedings of the MICCAI, pp. 592\u2013602 (2023)","DOI":"10.1007\/978-3-031-43993-3_57"},{"key":"59_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2023)","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"59_CR38","doi-asserted-by":"crossref","unstructured":"Zhu, L., Xue, Z., Jin, Z.E.A.: Make-a-volume: leveraging latent diffusion models for cross-modality 3D brain MRI synthesis. In: Proceedings of the MICCAI, pp. 592\u2013601 (2023)","DOI":"10.1007\/978-3-031-43999-5_56"}],"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\/978-3-032-04965-0_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:03:42Z","timestamp":1758233022000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04965-0_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032049643","9783032049650"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04965-0_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 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"}}]}}