{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T13:39:20Z","timestamp":1785764360806,"version":"3.56.0"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":33,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"EPSRC","doi-asserted-by":"publisher","award":["EP\/S026045\/1"],"award-info":[{"award-number":["EP\/S026045\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"EPSRC","doi-asserted-by":"publisher","award":["EP\/T026693\/1"],"award-info":[{"award-number":["EP\/T026693\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"EPSRC","doi-asserted-by":"publisher","award":["EP\/V026259\/1"],"award-info":[{"award-number":["EP\/V026259\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000275","name":"Leverhulme Trust","doi-asserted-by":"publisher","award":["ECF-2019-478"],"award-info":[{"award-number":["ECF-2019-478"]}],"id":[{"id":"10.13039\/501100000275","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Computed tomography (CT) imaging of the thorax is widely used for the detection and monitoring of pulmonary embolism (PE). However, CT images can contain artifacts due to the acquisition or the processes involved in image reconstruction. Radiologists often have to distinguish between such artifacts and actual PEs. We provide a proof of concept in the form of a scalable hypothesis testing method for CT, to enable quantifying uncertainty of possible PEs. In particular, we introduce a Bayesian Framework to quantify the uncertainty of an observed compact structure that can be identified as a PE. We assess the ability of the method to operate under high-noise environments and with insufficient data.<\/jats:p>","DOI":"10.1093\/pnasnexus\/pgad404","type":"journal-article","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T01:44:37Z","timestamp":1706060677000},"source":"Crossref","is-referenced-by-count":1,"title":["Uncertainty quantification in computed tomography pulmonary angiography"],"prefix":"10.1093","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8501-1339","authenticated-orcid":false,"given":"Adwaye M","family":"Rambojun","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences, University of Bath , Bath BA2 7JU , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1917-9437","authenticated-orcid":false,"given":"Hend","family":"Komber","sequence":"additional","affiliation":[{"name":"Royal United Hospital , Bath BA1 3NG , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1097-7897","authenticated-orcid":false,"given":"Jennifer","family":"Rossdale","sequence":"additional","affiliation":[{"name":"Royal United Hospital , Bath BA1 3NG , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jay","family":"Suntharalingam","sequence":"additional","affiliation":[{"name":"Royal United Hospital , Bath BA1 3NG , UK"},{"name":"Department of Life Sciences, University of Bath , Bath BA2 7JU , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2744-3509","authenticated-orcid":false,"given":"Jonathan C L","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Royal United Hospital , Bath BA1 3NG , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8523-353X","authenticated-orcid":false,"given":"Matthias J","family":"Ehrhardt","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, University of Bath , Bath BA2 7JU , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6296-6957","authenticated-orcid":false,"given":"Audrey","family":"Repetti","sequence":"additional","affiliation":[{"name":"School of Engineering and Physical Sciences, School of Mathematical and Computer Sciences, Heriot-Watt University , Edinburgh EH14 4AS , UK"},{"name":"Maxwell Institute for Mathematical Sciences , Edinburgh EH8 9BT , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,1,23]]},"reference":[{"issue":"1","key":"2024051108104277200_pgad404-B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s43586-021-00015-4","article-title":"X-ray computed tomography","volume":"1","author":"Withers","year":"2021","journal-title":"Nat Rev Methods Primers"},{"key":"2024051108104277200_pgad404-B2","year":"2005"},{"issue":"7","key":"2024051108104277200_pgad404-B3","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.amjmed.2015.01.023","article-title":"Predictive value of computed tomography in acute pulmonary embolism: systematic review and meta-analysis","volume":"128","author":"Meinel","year":"2015","journal-title":"Am J Med"},{"issue":"1","key":"2024051108104277200_pgad404-B4","doi-asserted-by":"crossref","first-page":"1801915","DOI":"10.1183\/13993003.01915-2018","article-title":"Chronic thromboembolic pulmonary hypertension","volume":"53","author":"Kim","year":"2019","journal-title":"Eur Respir J"},{"key":"2024051108104277200_pgad404-B5","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.ijcard.2018.10.001","article-title":"Incidence, mortality and bleeding rates associated with pulmonary embolism in england between 1997 and 2015","volume":"277","author":"Kempny","year":"2019","journal-title":"Int J Cardiol"},{"key":"2024051108104277200_pgad404-B6","doi-asserted-by":"crossref","DOI":"10.1137\/1.9781611976670","volume-title":"Computed tomography: algorithms, insight, and just enough theory","author":"Hansen","year":"2021"},{"key":"2024051108104277200_pgad404-B7","volume-title":"Computed tomography\u2014e-book: physical principles, clinical applications, and quality control","author":"Seeram","year":"2015"},{"key":"2024051108104277200_pgad404-B8","volume-title":"Statistical and computational inverse problems","author":"Kaipio","year":"2006"},{"key":"2024051108104277200_pgad404-B9","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-01458-2","volume-title":"Mathematical image processing","author":"Bredies","year":"2018","edition":"1st ed"},{"key":"2024051108104277200_pgad404-B10","volume-title":"The Bayesian choice: from decision-theoretic foundations to computational implementation","author":"Robert","year":"2007"},{"key":"2024051108104277200_pgad404-B11","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-4145-2","volume-title":"Monte Carlo statistical methods","author":"Robert","year":"2004"},{"key":"2024051108104277200_pgad404-B12"},{"issue":"4","key":"2024051108104277200_pgad404-B13","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1007\/s11222-015-9567-4","article-title":"Proximal Markov chain Monte Carlo algorithms","volume":"26","author":"Pereyra","year":"2016","journal-title":"Stat Comput"},{"key":"2024051108104277200_pgad404-B14","author":"Thouvenin","year":"2022"},{"issue":"2","key":"2024051108104277200_pgad404-B15","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1080\/10618600.2020.1826954","article-title":"Asymptotically exact data augmentation: models, properties, and algorithms","volume":"30","author":"Vono","year":"2021","journal-title":"J Comput Graph Stat"},{"key":"2024051108104277200_pgad404-B16","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1017\/S096249291600009X","article-title":"An introduction to continuous optimization for imaging","volume":"25","author":"Chambolle","year":"2016","journal-title":"Acta Numer"},{"key":"2024051108104277200_pgad404-B17","author":"Combettes","year":"2010"},{"issue":"6","key":"2024051108104277200_pgad404-B18","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/MSP.2014.2377273","article-title":"Playing with duality: an overview of recent primal-dual approaches for solving large-scale optimization problems","volume":"32","author":"Komodakis","year":"2015","journal-title":"IEEE Signal Process Mag"},{"issue":"1","key":"2024051108104277200_pgad404-B19","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1137\/16M1071249","article-title":"Maximum-a-posteriori estimation with Bayesian confidence regions","volume":"10","author":"Pereyra","year":"2017","journal-title":"SIAM J Imaging Sci"},{"key":"2024051108104277200_pgad404-B20","author":"Repetti","year":"2018"},{"issue":"1","key":"2024051108104277200_pgad404-B21","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1137\/18M1173629","article-title":"Scalable Bayesian uncertainty quantification in imaging inverse problems via convex optimization","volume":"12","author":"Repetti","year":"2019","journal-title":"SIAM J Imaging Sci"},{"key":"2024051108104277200_pgad404-B22","author":"Wolf","year":"2004"},{"issue":"4","key":"2024051108104277200_pgad404-B23","doi-asserted-by":"crossref","first-page":"778","DOI":"10.3390\/diagnostics13040778","article-title":"Auto-detection of motion artifacts on ct pulmonary angiograms with a physician-trained ai algorithm","volume":"13","author":"Dasegowda","year":"2023","journal-title":"Diagnostics"},{"issue":"1","key":"2024051108104277200_pgad404-B24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-95249-3","article-title":"Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis","volume":"11","author":"Soffer","year":"2021","journal-title":"Sci Rep"},{"issue":"1","key":"2024051108104277200_pgad404-B25","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1002\/acm2.13121","article-title":"A review on medical imaging synthesis using deep learning and its clinical applications","volume":"22","author":"Wang","year":"2021","journal-title":"J Appl Clin Med Phys"}],"container-title":["PNAS Nexus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/pnasnexus\/advance-article-pdf\/doi\/10.1093\/pnasnexus\/pgad404\/56346471\/pgad404.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/pnasnexus\/article-pdf\/3\/1\/pgad404\/57519509\/pgad404.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/pnasnexus\/article-pdf\/3\/1\/pgad404\/57519509\/pgad404.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T08:11:13Z","timestamp":1715415073000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/pnasnexus\/article\/doi\/10.1093\/pnasnexus\/pgad404\/7585855"}},"subtitle":[],"editor":[{"given":"Rui","family":"Reis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2023,12,21]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,12,21]]}},"URL":"https:\/\/doi.org\/10.1093\/pnasnexus\/pgad404","relation":{},"ISSN":["2752-6542"],"issn-type":[{"value":"2752-6542","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,1]]},"published":{"date-parts":[[2023,12,21]]},"article-number":"pgad404"}}