{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T07:08:15Z","timestamp":1781075295761,"version":"3.54.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031966248","type":"print"},{"value":"9783031966255","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"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\/978-3-031-96625-5_24","type":"book-chapter","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T07:11:44Z","timestamp":1754464304000},"page":"361-374","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["4DRGS: 4D Radiative Gaussian Splatting for\u00a0Efficient 3D Vessel Reconstruction from\u00a0Sparse-View Dynamic DSA Images"],"prefix":"10.1007","author":[{"given":"Zhentao","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruyi","family":"Zha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huangxuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongdong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiming","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"issue":"1","key":"24_CR1","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1177\/016173468400600107","volume":"6","author":"AH Andersen","year":"1984","unstructured":"Andersen, A.H., Kak, A.C.: Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm. Ultrason. Imaging 6(1), 81\u201394 (1984)","journal-title":"Ultrason. Imaging"},{"issue":"2","key":"24_CR2","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/34.121791","volume":"14","author":"P Besl","year":"1992","unstructured":"Besl, P., McKay, N.D.: A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239\u2013256 (1992). https:\/\/doi.org\/10.1109\/34.121791","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"24_CR3","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/2\/5\/055010","volume":"2","author":"A Biguri","year":"2016","unstructured":"Biguri, A., Dosanjh, M., Hancock, S., Soleimani, M.: Tigre: a MATLAB-GPU toolbox for CBCT image reconstruction. Biomed. Phys. Eng. Express 2(5), 055010 (2016)","journal-title":"Biomed. Phys. Eng. Express"},{"key":"24_CR4","doi-asserted-by":"publisher","unstructured":"Cai, Y., et al.: Radiative gaussian splatting for efficient x-ray novel view synthesis. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) Computer Vision. ECCV 2024. LNCS, vol. 15059, pp. 283\u2013299. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-73232-4_16","DOI":"10.1007\/978-3-031-73232-4_16"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Cai, Y., Wang, J., Yuille, A., Zhou, Z., Wang, A.: Structure-aware sparse-view x-ray 3d reconstruction. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.01062"},{"issue":"8","key":"24_CR6","first-page":"1507","volume":"18","author":"R Fahrig","year":"1997","unstructured":"Fahrig, R., Fox, A., Lownie, S., Holdsworth, D.: Use of a c-arm system to generate true three-dimensional computed rotational angiograms: preliminary in vitro and in vivo results. Am. J. Neuroradiol. 18(8), 1507\u20131514 (1997)","journal-title":"Am. J. Neuroradiol."},{"key":"24_CR7","unstructured":"Fang, Y., et al.: SNAF: Sparse-view CBCT reconstruction with neural attenuation fields. arXiv preprint arXiv:2211.17048 (2022)"},{"issue":"6","key":"24_CR8","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1364\/JOSAA.1.000612","volume":"1","author":"LA Feldkamp","year":"1984","unstructured":"Feldkamp, L.A., Davis, L.C., Kress, J.W.: Practical cone-beam algorithm. JOSA A 1(6), 612\u2013619 (1984)","journal-title":"JOSA A"},{"key":"24_CR9","unstructured":"Gao, Z., Planche, B., Zheng, M., Chen, X., Chen, T., Wu, Z.: DDGS-CT: direction-disentangled gaussian splatting for realistic volume rendering. arXiv preprint arXiv:2406.02518 (2024)"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Kak, A.C., Slaney, M.: Principles of computerized tomographic imaging. SIAM (2001)","DOI":"10.1137\/1.9780898719277"},{"issue":"4","key":"24_CR11","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3592433","volume":"42","author":"B Kerbl","year":"2023","unstructured":"Kerbl, B., Kopanas, G., Leimk\u00fchler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. 42(4), 139\u20131 (2023)","journal-title":"ACM Trans. Graph."},{"key":"24_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)"},{"issue":"6","key":"24_CR13","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.3174\/ajnr.A5161","volume":"38","author":"S Lang","year":"2017","unstructured":"Lang, S., et al.: 4d DSA for dynamic visualization of cerebral vasculature: a single-center experience in 26 cases. Am. J. Neuroradiol. 38(6), 1169\u20131176 (2017)","journal-title":"Am. J. Neuroradiol."},{"key":"24_CR14","unstructured":"Li, Y., Fu, X., Zhao, S., Jin, R., Zhou, S.K.: Sparse-view CT reconstruction with 3d gaussian volumetric representation. arXiv preprint arXiv:2312.15676 (2023)"},{"key":"24_CR15","unstructured":"Liu, Z., et al.: 3d vessel reconstruction from sparse-view dynamic DSA images via vessel probability guided attenuation learning. arXiv preprint arXiv:2405.10705 (2024)"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3d surface construction algorithm. In: Seminal Graphics: Pioneering Efforts that Shaped the Field, pp. 347\u2013353 (1998)","DOI":"10.1145\/280811.281026"},{"issue":"1","key":"24_CR17","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NERF: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99\u2013106 (2021)","journal-title":"Commun. ACM"},{"issue":"4","key":"24_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528223.3530127","volume":"41","author":"T M\u00fcller","year":"2022","unstructured":"M\u00fcller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. (ToG) 41(4), 1\u201315 (2022)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"24_CR19","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-2010), pp. 807\u2013814 (2010)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Park, S., Son, M., Jang, S., Ahn, Y.C., Kim, J.Y., Kang, N.: Temporal interpolation is all you need for dynamic neural radiance fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4212\u20134221 (2023)","DOI":"10.1109\/CVPR52729.2023.00410"},{"issue":"4","key":"24_CR21","first-page":"1","volume":"41","author":"D R\u00fcckert","year":"2022","unstructured":"R\u00fcckert, D., Wang, Y., Li, R., Idoughi, R., Heidrich, W.: NeAT: neural adaptive tomography. ACM Trans. Graph. (TOG) 41(4), 1\u201313 (2022)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"2","key":"24_CR22","doi-asserted-by":"publisher","first-page":"214","DOI":"10.3174\/ajnr.A6860","volume":"42","author":"K Ruedinger","year":"2021","unstructured":"Ruedinger, K., Schafer, S., Speidel, M., Strother, C.: 4D-DSA: development and current neurovascular applications. Am. J. Neuroradiol. 42(2), 214\u2013220 (2021)","journal-title":"Am. J. Neuroradiol."},{"issue":"10","key":"24_CR23","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.3174\/ajnr.A4359","volume":"36","author":"C Sandoval-Garcia","year":"2015","unstructured":"Sandoval-Garcia, C., Royalty, K., Aagaard-Kienitz, B., Schafer, S., Yang, P., Strother, C.: A comparison of 4d DSA with 2d and 3d DSA in the analysis of normal vascular structures in a canine model. Am. J. Neuroradiol. 36(10), 1959\u20131963 (2015)","journal-title":"Am. J. Neuroradiol."},{"key":"24_CR24","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1136\/neurintsurg-2014-011534","volume":"8","author":"C Sandoval-Garcia","year":"2015","unstructured":"Sandoval-Garcia, C., et al.: 4D DSA a new technique for arteriovenous malformation evaluation: a feasibility study. J. Neurointerv. Surg. 8, 300\u2013304 (2015)","journal-title":"J. Neurointerv. Surg."},{"issue":"17","key":"24_CR25","doi-asserted-by":"publisher","first-page":"4777","DOI":"10.1088\/0031-9155\/53\/17\/021","volume":"53","author":"EY Sidky","year":"2008","unstructured":"Sidky, E.Y., Pan, X.: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys. Med. Biol. 53(17), 4777 (2008)","journal-title":"Phys. Med. Biol."},{"issue":"4","key":"24_CR26","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":"24_CR27","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1109\/TCI.2023.3281196","volume":"9","author":"Q Wu","year":"2023","unstructured":"Wu, Q., Feng, R., Wei, H., Yu, J., Zhang, Y.: Self-supervised coordinate projection network for sparse-view computed tomography. IEEE Trans. Comput. Imaging 9, 517\u2013524 (2023)","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"24_CR28","doi-asserted-by":"crossref","unstructured":"Xu, Y., et al.: GRM: large gaussian reconstruction model for efficient 3d reconstruction and generation. arXiv preprint arXiv:2403.14621 (2024)","DOI":"10.1007\/978-3-031-72633-0_1"},{"key":"24_CR29","doi-asserted-by":"crossref","unstructured":"Zang, G., Idoughi, R., Li, R., Wonka, P., Heidrich, W.: Intratomo: self-supervised learning-based tomography via sinogram synthesis and prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1960\u20131970 (2021)","DOI":"10.1109\/ICCV48922.2021.00197"},{"key":"24_CR30","unstructured":"Zha, R., Lin, T.J., Cai, Y., Cao, J., Zhang, Y., Li, H.: R$$^2$$-gaussian: rectifying radiative gaussian splatting for tomographic reconstruction. arXiv preprint arXiv:2405.20693 (2024)"},{"key":"24_CR31","doi-asserted-by":"publisher","unstructured":"Zha, R., Zhang, Y., Li, H.: NAF: neural attenuation fields for sparse-view CBCT reconstruction. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention. MICCAI 2022. LNCS, vol. 13436, pp. 442\u2013452. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16446-0_42","DOI":"10.1007\/978-3-031-16446-0_42"},{"key":"24_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, S., et al.: Togs: Gaussian splatting with temporal opacity offset for real-time 4D DSA rendering. arXiv preprint arXiv:2403.19586 (2024)","DOI":"10.1109\/JBHI.2025.3575613"},{"issue":"10","key":"24_CR33","first-page":"1","volume":"3","author":"H Zhao","year":"2022","unstructured":"Zhao, H., et al.: Self-supervised learning enables 3d digital subtraction angiography reconstruction from ultra-sparse 2d projection views: a multicenter study. Cell Rep. Med. 3(10), 1\u201339 (2022)","journal-title":"Cell Rep. Med."}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96625-5_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T13:21:31Z","timestamp":1777468891000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96625-5_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9783031966248","9783031966255"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96625-5_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]},"assertion":[{"value":"7 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"25 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 May 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmi2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}