{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:32:37Z","timestamp":1777656757561,"version":"3.51.4"},"publisher-location":"Cham","reference-count":23,"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_2","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"14-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["3DGR-CAR: Coronary Artery Reconstruction from\u00a0Ultra-sparse 2D X-Ray Views with\u00a0a\u00a03D Gaussians Representation"],"prefix":"10.1007","author":[{"given":"Xueming","family":"Fu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingtai","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fenghe","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyue","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gao-Jun","family":"Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. Kevin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"2_CR1","unstructured":"Fang, Y., et al.: SNAF: sparse-view CBCT reconstruction with neural attenuation fields. arXiv preprint arXiv:2211.17048 (2022)"},{"issue":"6","key":"2_CR2","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":"2_CR3","doi-asserted-by":"crossref","unstructured":"Gharleghi, R., et al.: Automated segmentation of normal and diseased coronary arteries-the asoca challenge. Comput. Med. Imaging Graph. 97, 102049 (2022)","DOI":"10.1016\/j.compmedimag.2022.102049"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Gharleghi, R., et al.: Computed tomography coronary angiogram images, annotations and associated data of normal and diseased arteries. arXiv preprint arXiv:2211.01859 (2022)","DOI":"10.1038\/s41597-023-02016-2"},{"issue":"2","key":"2_CR5","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1088\/0031-9155\/45\/2\/306","volume":"45","author":"M Grass","year":"2000","unstructured":"Grass, M., K\u00f6hler, T., Proksa, R.: 3D Cone-beam CT reconstruction for circular trajectories. Phys. Med. Biol. 45(2), 329\u2013347 (2000)","journal-title":"Phys. Med. Biol."},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Kerbl, B., Kopanas, G., Leimk\u00fchler, T., Drettakis, G.: 3D Gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. 42(4), 1\u201314 (2023)","DOI":"10.1145\/3592433"},{"key":"2_CR7","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":"2_CR8","unstructured":"Liu, Y., Li, C., Yang, C., Yuan, Y.: Endogaussian: Gaussian splatting for deformable surgical scene reconstruction. arXiv preprint arXiv:2401.12561 (2024)"},{"key":"2_CR9","unstructured":"Maas, K.W., Pezzotti, N., Vermeer, A.J., Ruijters, D., Vilanova, A.: Nerf for 3d reconstruction from x-ray angiography: possibilities and limitations. In: VCBM 2023: Eurographics Workshop on Visual Computing for Biology and Medicine, pp. 29\u201340. Eurographics Association (2023)"},{"issue":"1","key":"2_CR10","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":"10","key":"2_CR11","doi-asserted-by":"publisher","first-page":"2787","DOI":"10.1088\/0031-9155\/45\/10\/305","volume":"45","author":"N Milickovic","year":"2000","unstructured":"Milickovic, N., Baltas, D., Giannouli, S., Lahanas, M., Zamboglou, N.: Ct imaging based digitally reconstructed radiographs and their application in brachytherapy. Phys. Med. Biol. 45(10), 2787 (2000)","journal-title":"Phys. Med. Biol."},{"key":"2_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"2_CR13","unstructured":"Roth, G.A., et al.: Global burden of cardiovascular diseases and risk factors, 1990\u20132019: update from the GBD 2019 study. J. Am. Coll. Cardiol. 76(25), 2982\u20133021 (2020)"},{"issue":"6","key":"2_CR14","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1161\/01.CIR.0000024109.12658.D4","volume":"106","author":"TJ Ryan","year":"2002","unstructured":"Ryan, T.J.: The coronary angiogram and its seminal contributions to cardiovascular medicine over five decades. Circulation 106(6), 752\u2013756 (2002)","journal-title":"Circulation"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Serruys, P.W., et al.: Coronary computed tomographic angiography for complete assessment of coronary artery disease: JACC state-of-the-art review. J. Am. Coll. Cardiol. 78(7), 713\u2013736 (2021)","DOI":"10.1016\/j.jacc.2021.06.019"},{"key":"2_CR16","unstructured":"Shen, L., Pauly, J., Xing, L.: Nerp: implicit neural representation learning with prior embedding for sparsely sampled image reconstruction. IEEE Trans. Neural Netw. Learn. Syst. (2022)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Shit, S., et al.: cldice-a novel topology-preserving loss function for tubular structure segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16560\u201316569 (2021)","DOI":"10.1109\/CVPR46437.2021.01629"},{"issue":"2","key":"2_CR18","first-page":"119","volume":"14","author":"EY Sidky","year":"2006","unstructured":"Sidky, E.Y., Kao, C.M., Pan, X.: Accurate image reconstruction from few-views and limited-angle data in divergent-beam ct. J. Xray Sci. Technol. 14(2), 119\u2013139 (2006)","journal-title":"J. Xray Sci. Technol."},{"key":"2_CR19","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"issue":"4","key":"2_CR20","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/S0146-664X(76)80033-0","volume":"5","author":"W Wagner","year":"1976","unstructured":"Wagner, W.: Reconstruction of object layers from their x-ray projections: a simulation study. Comput. Graph. Image Process. 5(4), 470\u2013483 (1976)","journal-title":"Comput. Graph. Image Process."},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Zeng, A., et al.: Imagecas: a large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images. Comput. Med. Imaging Graph. 109, 102287 (2023)","DOI":"10.1016\/j.compmedimag.2023.102287"},{"key":"2_CR22","doi-asserted-by":"publisher","unstructured":"Zha, R., Zhang, Y., Li, H.: NAF: neural attenuation fields for\u00a0sparse-view CBCT reconstruction. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part VI, 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":"2_CR23","unstructured":"Zhu, L., Wang, Z., Jin, Z., Lin, G., Yu, L.: Deformable endoscopic tissues reconstruction with Gaussian splatting. arXiv preprint arXiv:2401.11535 (2024)"}],"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_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T09:03:45Z","timestamp":1733562225000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72104-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721038","9783031721045"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72104-5_2","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"}}]}}