{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T10:24:47Z","timestamp":1758450287118,"version":"3.44.0"},"publisher-location":"Cham","reference-count":19,"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_11","type":"book-chapter","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:32:46Z","timestamp":1758389566000},"page":"110-119","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Causality-Driven Spatio-Temporal Generator for\u00a0Multi-phase Contrast-Enhanced CT Synthesis"],"prefix":"10.1007","author":[{"given":"Qikui","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanyan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"11_CR1","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1007\/978-3-031-72390-2_37","volume-title":"MICCAI 2024","author":"I Abdelhalim","year":"2024","unstructured":"Abdelhalim, I., Abou El-Ghar, M., Dwyer, A., Ouseph, R., Contractor, S., El-Baz, A.: A new non-invasive AI-based diagnostic system for automated diagnosis of acute renal rejection in kidney transplantation: analysis of ADC maps extracted from matched 3D iso-regions of the transplanted kidney. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15012, pp. 390\u2013398. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72390-2_37"},{"issue":"11","key":"11_CR2","doi-asserted-by":"publisher","first-page":"8786","DOI":"10.1007\/s00330-021-07877-y","volume":"31","author":"H Bae","year":"2021","unstructured":"Bae, H., et al.: Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists. Eur. Radiol. 31(11), 8786\u20138796 (2021)","journal-title":"Eur. Radiol."},{"key":"11_CR3","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12873\u201312883 (2021)","DOI":"10.1109\/CVPR46437.2021.01268"},{"issue":"4","key":"11_CR5","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1097\/00004728-200607000-00007","volume":"30","author":"NS Holalkere","year":"2006","unstructured":"Holalkere, N.S., Sahani, D.V., Blake, M.A., Halpern, E.F., Hahn, P.F., Mueller, P.R.: Characterization of small liver lesions: added role of MR after MDCT. J. Comput. Assist. Tomogr. 30(4), 591\u2013596 (2006)","journal-title":"J. Comput. Assist. Tomogr."},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Hu, T., et al.: Aorta-aware GAN for non-contrast to artery contrasted CT translation and its application to abdominal aortic aneurysm detection. Int. J. Comput. Assist. Radiol. Surg.,\u00a01\u20139 (2022)","DOI":"10.1007\/s11548-021-02492-0"},{"key":"11_CR7","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1007\/978-3-031-72114-4_38","volume-title":"MICCAI 2024","author":"W Huang","year":"2024","unstructured":"Huang, W., et al.: LIDIA: precise liver tumor diagnosis on multi-phase contrast-enhanced CT via iterative fusion and asymmetric contrastive learning. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15009, pp. 394\u2013404. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72114-4_38"},{"key":"11_CR8","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1007\/978-3-031-72384-1_69","volume-title":"MICCAI 2024","author":"A Kim","year":"2024","unstructured":"Kim, A., et al.: S-SYNTH: knowledge-based, synthetic generation of skin images. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15003, pp. 734\u2013744. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72384-1_69"},{"issue":"9","key":"11_CR9","doi-asserted-by":"publisher","first-page":"2359","DOI":"10.1681\/ASN.2006060601","volume":"17","author":"P Marckmann","year":"2006","unstructured":"Marckmann, P., et al.: Nephrogenic systemic fibrosis: suspected causative role of gadodiamide used for contrast-enhanced magnetic resonance imaging. J. Am. Soc. Nephrol. 17(9), 2359\u20132362 (2006)","journal-title":"J. Am. Soc. Nephrol."},{"key":"11_CR10","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"key":"11_CR11","unstructured":"Mitrovic, J., McWilliams, B., Walker, J., Buesing, L., Blundell, C.: Representation learning via invariant causal mechanisms. arXiv preprint arXiv:2010.07922 (2020)"},{"issue":"12","key":"11_CR12","doi-asserted-by":"publisher","first-page":"2720","DOI":"10.1109\/TBME.2018.2814538","volume":"65","author":"D Nie","year":"2018","unstructured":"Nie, D., et al.: Medical image synthesis with deep convolutional adversarial networks. IEEE Trans. Biomed. Eng. 65(12), 2720\u20132730 (2018)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"11_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.03.072","volume":"538","author":"NC Ristea","year":"2023","unstructured":"Ristea, N.C., et al.: CyTran: a cycle-consistent transformer with multi-level consistency for non-contrast to contrast CT translation. Neurocomputing 538, 126211 (2023)","journal-title":"Neurocomputing"},{"key":"11_CR14","unstructured":"Tschannen, M., Bachem, O., Lucic, M.: Recent advances in autoencoder-based representation learning. arXiv preprint arXiv:1812.05069 (2018)"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"11_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.111638","volume":"165","author":"Q Zhu","year":"2025","unstructured":"Zhu, Q., Wang, Y., Zhu, S., Du, B.: Partial consistent adversarial unified framework for unsupervised non-contrast CT cross-domain adaptation and segmentation. Pattern Recogn. 165, 111638 (2025)","journal-title":"Pattern Recogn."},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Wentland, A.L., Li, S.: Contrast-aware network with aggregated-interacted transformer and multi-granularity aligned contrastive learning for synthesizing contrast-enhanced abdomen CT imaging. IEEE Trans. Comput. Imaging (2025)","DOI":"10.1109\/TCI.2025.3540711"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Zhu, S., Du, B., Wang, Y.: Cross-domain distribution adversarial diffusion model for synthesizing contrast-enhanced abdomen CT imaging. Pattern Recognit., 111695 (2025)","DOI":"10.1016\/j.patcog.2025.111695"},{"key":"11_CR19","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1007\/978-3-031-72069-7_50","volume-title":"2024","author":"X Zhu","year":"2024","unstructured":"Zhu, X., Zhang, W., Li, Y., O\u2019Donnell, L.J., Zhang, F.: When diffusion MRI meets diffusion model: a novel deep generative model for diffusion MRI generation. In: Linguraru, M.G., et al. (eds.) 2024. LNCS, vol. 15002, pp. 530\u2013540. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72069-7_50"}],"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_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:32:50Z","timestamp":1758389570000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04947-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032049469","9783032049476"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04947-6_11","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"}}]}}