{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:18:57Z","timestamp":1743063537801,"version":"3.40.3"},"publisher-location":"Wiesbaden","reference-count":12,"publisher":"Springer Fachmedien Wiesbaden","isbn-type":[{"type":"print","value":"9783658331979"},{"type":"electronic","value":"9783658331986"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-658-33198-6_80","type":"book-chapter","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T20:03:22Z","timestamp":1614369802000},"page":"330-335","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging"],"prefix":"10.1007","author":[{"given":"Jan-Hinrich","family":"N\u00f6lke","sequence":"first","affiliation":[]},{"given":"Tim","family":"Adler","sequence":"additional","affiliation":[]},{"given":"Janek","family":"Gr\u00f6hl","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Kirchner","sequence":"additional","affiliation":[]},{"given":"Lynton","family":"Ardizzone","sequence":"additional","affiliation":[]},{"given":"Carsten","family":"Rother","sequence":"additional","affiliation":[]},{"given":"Ullrich","family":"K\u00f6the","sequence":"additional","affiliation":[]},{"given":"Lena","family":"Maier-Hein","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,27]]},"reference":[{"key":"80_CR1","doi-asserted-by":"crossref","unstructured":"Zackrisson S, van de Ven SMWY, Gambhir SS. Light in and sound out: emerging translational strategies for photoacoustic imaging. Cancer Res. 2014;74(4):979\u20131004.","DOI":"10.1158\/0008-5472.CAN-13-2387"},{"key":"80_CR2","doi-asserted-by":"crossref","unstructured":"Yang C, Lan H, Gao F, et al. Deep learning for photoacoustic imaging: a survey. arXiv:200804221 [cs, eess]. 2020;.","DOI":"10.1016\/j.pacs.2020.100215"},{"key":"80_CR3","doi-asserted-by":"crossref","unstructured":"Tarvainen T, Pulkkinen A, Cox BT, et al. Bayesian image reconstruction in quantitative photoacoustic tomography. IEEE Trans Med Imaging. 2013 Dec;32(12):2287\u20132298.","DOI":"10.1109\/TMI.2013.2280281"},{"key":"80_CR4","doi-asserted-by":"crossref","unstructured":"Tick J, Pulkkinen A, Tarvainen T. Image reconstruction with uncertainty quantification in photoacoustic tomography. J Acoust Soc Am. 2016;139(4):1951\u20131961.","DOI":"10.1121\/1.4945990"},{"key":"80_CR5","doi-asserted-by":"crossref","unstructured":"Gr\u00f6hl J, Kirchner T, Adler T, et al. Confidence estimation for machine learningbased quantitative photoacoustics. J Imaging. 2018;4(12):147.","DOI":"10.3390\/jimaging4120147"},{"key":"80_CR6","doi-asserted-by":"crossref","unstructured":"Godefroy G, Arnal B, Bossy E. Solving the visibility problem in photoacoustic imaging with a deep learning approach providing prediction uncertainties. arXiv:200613096 [physics]. 2020;.","DOI":"10.1016\/j.pacs.2020.100218"},{"key":"80_CR7","unstructured":"Ardizzone L, L\u00fcth C, Kruse J, et al. Guided image generation with conditional invertible neural networks. arXiv:19070233092 [cs]. 2019;."},{"key":"80_CR8","doi-asserted-by":"crossref","unstructured":"Fang Q, Boas DA. Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units. Opt Express. 2009;17(22):20178\u201320190.","DOI":"10.1364\/OE.17.020178"},{"key":"80_CR9","unstructured":"Kingma DP, Dhariwal P. Glow: generative ow with invertible 1x1 convolutions. arXiv:180703039 [cs, stat]. 2018;."},{"key":"80_CR10","unstructured":"Ardizzone L, Kruse J, Wirkert S, et al. Analyzing inverse problems with invertible neural networks. arXiv:180804730 [cs, stat]. 2019;."},{"key":"80_CR11","doi-asserted-by":"crossref","unstructured":"Adler TJ, Ardizzone L, Vemuri A, et al. Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks. Int J Comput Assist Radiol Surg. 2019;14(6):997\u20131007.","DOI":"10.1007\/s11548-019-01939-9"},{"key":"80_CR12","doi-asserted-by":"crossref","unstructured":"Shao P, Cox B, Zemp R. Estimating optical absorption, scattering, and Grueneisen distributions with multiple-illumination photoacoustic tomography. Appl Opt. 2011;50:3145\u201354.","DOI":"10.1364\/AO.50.003145"}],"container-title":["Informatik aktuell","Bildverarbeitung f\u00fcr die Medizin 2021"],"original-title":[],"language":"de","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-658-33198-6_80","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T20:22:45Z","timestamp":1614370965000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-658-33198-6_80"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783658331979","9783658331986"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-658-33198-6_80","relation":{},"ISSN":["1431-472X"],"issn-type":[{"type":"print","value":"1431-472X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"27 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}