{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:38:19Z","timestamp":1783528699276,"version":"3.55.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031723773","type":"print"},{"value":"9783031723780","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\/978-3-031-72378-0_2","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T07:02:53Z","timestamp":1727852573000},"page":"14-23","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Diffusion-Based Generative Image Outpainting for\u00a0Recovery of\u00a0FOV-Truncated CT Images"],"prefix":"10.1007","author":[{"given":"Michelle Espranita","family":"Liman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian J.","family":"Fintelmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"2_CR1","unstructured":"Framingham heart study. https:\/\/www.framinghamheartstudy.org\/"},{"key":"2_CR2","unstructured":"Vlsp. https:\/\/www.vumc.org\/radiology\/lung"},{"issue":"1","key":"2_CR3","doi-asserted-by":"publisher","first-page":"4317","DOI":"10.1038\/s41467-023-40024-3","volume":"14","author":"A Babic","year":"2023","unstructured":"Babic, A., et al.: Adipose tissue and skeletal muscle wasting precede clinical diagnosis of pancreatic cancer. Nat. Commun. 14(1), 4317 (2023)","journal-title":"Nat. Commun."},{"issue":"1","key":"2_CR4","doi-asserted-by":"publisher","first-page":"e210080","DOI":"10.1148\/ryai.210080","volume":"4","author":"CP Bridge","year":"2022","unstructured":"Bridge, C.P., et al.: A fully automated deep learning pipeline for multi-vertebral level quantification and characterization of muscle and adipose tissue on chest CT scans. Radiol.: Artif. Intell. 4(1), e210080 (2022). https:\/\/doi.org\/10.1148\/ryai.210080","journal-title":"Radiol.: Artif. Intell."},{"key":"2_CR5","unstructured":"Chen, N., Zhang, Y., Zen, H., Weiss, R.J., Norouzi, M., Chan, W.: WaveGrad: estimating gradients for waveform generation (2020)"},{"key":"2_CR6","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database, pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2_CR7","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis (2021)"},{"key":"2_CR8","unstructured":"\u00c9ric Fourni\u00e9, Baer-Beck, M., Stierstorfer, K.: CT field of view extension using combined channels extension and deep learning methods (2019)"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR10","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium (2018)"},{"key":"2_CR11","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models (2020)"},{"key":"2_CR12","series-title":"Informatik aktuell","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-658-29267-6_40","volume-title":"Bildverarbeitung f\u00fcr die Medizin 2020","author":"Y Huang","year":"2020","unstructured":"Huang, Y., Gao, L., Preuhs, A., Maier, A.: Field of view extension in computed tomography using deep learning prior. In: Tolxdorff, T., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K.H., Palm, C. (eds.) Bildverarbeitung f\u00fcr die Medizin 2020. Informatik aktuell, pp. 186\u2013191. Springer, Wiesbaden (2020). https:\/\/doi.org\/10.1007\/978-3-658-29267-6_40"},{"key":"#cr-split#-2_CR13.1","doi-asserted-by":"crossref","unstructured":"Kazerooni, E.A., et al.: ACR-STR practice parameter for the performance and reporting of lung cancer screening thoracic computed tomography (CT): 2014 (resolution 4)*. J. Thoracic Imaging 29","DOI":"10.1097\/RTI.0000000000000097"},{"key":"#cr-split#-2_CR13.2","unstructured":"(5) (2014). https:\/\/journals.lww.com\/thoracicimaging\/fulltext\/2014\/09000\/acr_str_practice_parameter_for_the_performance_and.12.aspx"},{"issue":"6","key":"2_CR14","doi-asserted-by":"publisher","first-page":"065041","DOI":"10.1088\/2057-1976\/ac31cb","volume":"7","author":"JHJ Ketola","year":"2021","unstructured":"Ketola, J.H.J., Heino, H., Juntunen, M.A.K., Nieminen, M.T., Siltanen, S., Inkinen, S.I.: Generative adversarial networks improve interior computed tomography angiography reconstruction. Biomed. Phys. Eng. Express 7(6), 065041 (2021)","journal-title":"Biomed. Phys. Eng. Express"},{"key":"2_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, N., Zhang, L., Du, B., Tao, D.: Recurrent feature reasoning for image inpainting (2020)","DOI":"10.1109\/CVPR42600.2020.00778"},{"issue":"18","key":"2_CR17","doi-asserted-by":"publisher","first-page":"3941","DOI":"10.3390\/s19183941","volume":"19","author":"Z Li","year":"2019","unstructured":"Li, Z., et al.: Promising generative adversarial network based sinogram inpainting method for ultra-limited-angle computed tomography imaging. Sens. (Basel) 19(18), 3941 (2019)","journal-title":"Sens. (Basel)"},{"issue":"2","key":"2_CR18","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1148\/radiol.2020201640","volume":"298","author":"K Magudia","year":"2021","unstructured":"Magudia, K., et al.: Population-scale CT-based body composition analysis of a large outpatient population using deep learning to derive age-, sex-, and race-specific reference curves. Radiology 298(2), 319\u2013329 (2021)","journal-title":"Radiology"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Saharia, C., et al.: Palette: image-to-image diffusion models (2022)","DOI":"10.1145\/3528233.3530757"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement (2021)","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"2_CR21","unstructured":"Sohl-Dickstein, J., Weiss, E.A., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics (2015)"},{"issue":"5","key":"2_CR22","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/TMI.2021.3058281","volume":"40","author":"Y Tang","year":"2021","unstructured":"Tang, Y., et al.: Body part regression with self-supervision. IEEE Trans. Med. Imaging 40(5), 1499\u20131507 (2021). https:\/\/doi.org\/10.1109\/TMI.2021.3058281","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"2_CR23","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1097\/RTI.0000000000000428","volume":"35","author":"AS Troschel","year":"2020","unstructured":"Troschel, A.S., et al.: Computed tomography-based body composition analysis and its role in lung cancer care. J. Thorac. Imaging 35(2), 91\u2013100 (2020)","journal-title":"J. Thorac. Imaging"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Xu, K., et al.: Body composition assessment with limited field-of-view computed tomography: a semantic image extension perspective (2023)","DOI":"10.1016\/j.media.2023.102852"}],"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\/978-3-031-72378-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T07:30:14Z","timestamp":1771054214000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72378-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723773","9783031723780"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72378-0_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"}}]}}