{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T13:08:21Z","timestamp":1781010501728,"version":"3.54.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:00Z","timestamp":1780963200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:00Z","timestamp":1780963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e4tsmedizin Rostock"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>To investigate whether a CT pulmonary angiography (CTPA) protocol with reduced radiation dose and deep-learning based image reconstruction (DLIR) is non-inferior in image quality to standard-dose CTPA using iterative reconstruction. A phantom study was conducted to estimate the additional radiation dose reduction enabled by high-strength deep learning-based image reconstruction (DLIR-H) compared to adaptive statistical iterative reconstruction (ASiR-V 90%). Medium and large phantoms were used to simulate different body sizes. Subsequently, we reduced radiation dose of our clinical CTPA protocol and transitioned to DLIR for image reconstruction. We retrospectively analyzed 307 consecutive patients who were examined before (n\u2009=\u2009152) and after (n\u2009=\u2009155) this clinically driven change in the CTPA protocol. Objective image quality was quantified and subjective image quality was rated by two radiologists. The non-inferiority margin was pre-specified as a\u2009&lt;\u20095% difference in image quality parameters. In the phantom, DLIR-H allowed radiation dose to be reduced by up to 71% with equivalent or higher signal-to-noise-ratio (SNR) compared to standard-dose examinations reconstructed with ASiR-V 90%. In the patient cohort, radiation dose was reduced by 41% (median DLP 116 vs. 68 mGy*cm; effective dose 1.69 vs. 0.99 mSv, p\u2009&lt;\u20090.001). In the modified protocol, median SNR was superior for the central pulmonary artery (13.6 vs. 22.3) and non-inferior for the segmental pulmonary arteries (16.4 vs. 16.8). Subjective image quality averaged over both readers was superior with the modified protocol. Compared to state-of-the-art iterative reconstruction, DLIR allows radiation dose for CTPA to be reduced by an additional 41% with non-inferior image quality.<\/jats:p>","DOI":"10.1038\/s41598-026-56545-y","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T10:45:38Z","timestamp":1781001938000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep-learning based image reconstruction enables reduced dose CT pulmonary angiography with non-inferior image quality"],"prefix":"10.1038","volume":"16","author":[{"given":"Sina","family":"Sender","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul J.","family":"B\u00f6ttcher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ann-Christin","family":"Klemenz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Geyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justus","family":"Hillebrand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"L\u00fctgens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto","family":"Lorbeer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathias","family":"Manzke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc-Andr\u00e9","family":"Weber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix G.","family":"Meinel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"56545_CR1","doi-asserted-by":"publisher","first-page":"2547","DOI":"10.1007\/s00330-015-3679-2","volume":"25","author":"LJ Zhang","year":"2015","unstructured":"Zhang, L. J. et al. Computed tomography of acute pulmonary embolism: State-of-the-art. Eur. Radiol. 25, 2547\u20132557 (2015).","journal-title":"Eur. Radiol."},{"key":"56545_CR2","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.rcl.2024.08.003","volume":"63","author":"K Batra","year":"2025","unstructured":"Batra, K., Kay, F. U., Sibley, R. C. & Peshock, R. M. Imaging of acute pulmonary embolism: An update. Radiol. Clin. North Am. 63, 207\u2013222 (2025).","journal-title":"Radiol. Clin. North Am."},{"key":"56545_CR3","doi-asserted-by":"publisher","first-page":"225","DOI":"10.21037\/cdt.2017.12.01","volume":"8","author":"AJE Moore","year":"2018","unstructured":"Moore, A. J. E. et al. Imaging of acute pulmonary embolism: An update. Cardiovasc. Diagn. Ther. 8, 225\u2013243 (2018).","journal-title":"Cardiovasc. Diagn. Ther."},{"key":"56545_CR4","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1148\/radiol.12120414","volume":"267","author":"F Pontana","year":"2013","unstructured":"Pontana, F. et al. Reduced-dose low-voltage chest CT angiography with sinogram-affirmed iterative reconstruction versus standard-dose filtered back projection. Radiology 267, 609\u2013618 (2013).","journal-title":"Radiology"},{"key":"56545_CR5","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1148\/radiol.2015141507","volume":"279","author":"JV Spearman","year":"2016","unstructured":"Spearman, J. V. et al. Effect of automated attenuation-based tube voltage selection on radiation dose at CT: An observational study on a global scale. Radiology 279, 167\u2013174 (2016).","journal-title":"Radiology"},{"key":"56545_CR6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0146949","volume":"11","author":"BO Sabel","year":"2016","unstructured":"Sabel, B. O. et al. High-pitch CT pulmonary angiography in third generation dual-source CT: Image quality in an unselected patient population. PLoS One 11, e0146949 (2016).","journal-title":"PLoS One"},{"key":"56545_CR7","doi-asserted-by":"publisher","first-page":"5280","DOI":"10.1016\/j.acra.2024.06.028","volume":"31","author":"P Pannenbecker","year":"2024","unstructured":"Pannenbecker, P. et al. The best of both worlds: Ultra-high-pitch pulmonary angiography with free-breathing technique by means of photon-counting detector CT for diagnosis of acute pulmonary embolism. Acad. Radiol. 31, 5280\u20135288 (2024).","journal-title":"Acad. Radiol."},{"key":"56545_CR8","doi-asserted-by":"publisher","first-page":"6544","DOI":"10.1007\/s00330-024-10724-5","volume":"34","author":"M Remy-Jardin","year":"2024","unstructured":"Remy-Jardin, M. et al. Diagnosis of acute pulmonary embolism: When photon-counting-detector CT replaces energy-integrating-detector CT in daily routine. Eur. Radiol. 34, 6544\u20136555 (2024).","journal-title":"Eur. Radiol."},{"key":"56545_CR9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0162429","volume":"11","author":"A Laqmani","year":"2016","unstructured":"Laqmani, A. et al. CT pulmonary angiography at reduced radiation exposure and contrast material volume using iterative model reconstruction and iDose4 technique in comparison to FBP. PLoS. One. 11, e0162429 (2016).","journal-title":"PLoS. One."},{"key":"56545_CR10","doi-asserted-by":"publisher","first-page":"3260","DOI":"10.1007\/s00330-014-3365-9","volume":"24","author":"GM Lu","year":"2014","unstructured":"Lu, G. M. et al. High-pitch computed tomography pulmonary angiography with iterative reconstruction at 80 kVp and 20 ml contrast agent volume. Eur. Radiol. 24, 3260\u20133268 (2014).","journal-title":"Eur. Radiol."},{"key":"56545_CR11","doi-asserted-by":"publisher","first-page":"1665","DOI":"10.1007\/s00330-014-3563-5","volume":"25","author":"X Montet","year":"2015","unstructured":"Montet, X. et al. Image quality of low mA CT pulmonary angiography reconstructed with model based iterative reconstruction versus standard CT pulmonary angiography reconstructed with filtered back projection: An equivalency trial. Eur. Radiol. 25, 1665\u20131671 (2015).","journal-title":"Eur. Radiol."},{"key":"56545_CR12","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.diii.2014.08.006","volume":"96","author":"F Pontana","year":"2015","unstructured":"Pontana, F. et al. CT pulmonary angiogram with 60% dose reduction: Influence of iterative reconstructions on image quality. Diagn. Interv. Imaging. 96, 487\u2013493 (2015).","journal-title":"Diagn. Interv. Imaging."},{"key":"56545_CR13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0162716","volume":"11","author":"A Sauter","year":"2016","unstructured":"Sauter, A. et al. Ultra low dose CT pulmonary angiography with iterative reconstruction. PLoS. One. 11, e0162716 (2016).","journal-title":"PLoS. One."},{"key":"56545_CR14","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1148\/radiol.2015132766","volume":"276","author":"LL Geyer","year":"2015","unstructured":"Geyer, L. L. et al. State of the art: Iterative CT reconstruction techniques. Radiology 276, 339\u2013357 (2015).","journal-title":"Radiology"},{"key":"56545_CR15","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1055\/a-1248-2556","volume":"193","author":"C Arndt","year":"2021","unstructured":"Arndt, C. et al. CT-Bildrekonstruktion mit deep learning in der klinischen Praxis. RoFo: Fortschr. Gebiete Rontgenstrahlen Nuklearmedizin 193, 252\u2013261 (2021).","journal-title":"RoFo: Fortschr. Gebiete Rontgenstrahlen Nuklearmedizin"},{"key":"56545_CR16","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.221257","volume":"306","author":"LR Koetzier","year":"2023","unstructured":"Koetzier, L. R. et al. Deep learning image reconstruction for CT: Technical principles and clinical prospects. Radiology 306, e221257 (2023).","journal-title":"Radiology"},{"key":"56545_CR17","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s40134-022-00399-5","volume":"10","author":"TP Szczykutowicz","year":"2022","unstructured":"Szczykutowicz, T. P., Toia, G. V., Dhanantwari, A. & Nett, B. A review of deep learning CT reconstruction: Concepts, limitations, and promise in clinical practice. Curr. Radiol. Rep. 10, 101\u2013115 (2022).","journal-title":"Curr. Radiol. Rep."},{"key":"56545_CR18","doi-asserted-by":"publisher","first-page":"2494","DOI":"10.1038\/s41598-024-52517-2","volume":"14","author":"AC Klemenz","year":"2024","unstructured":"Klemenz, A. C. et al. Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction. Sci. Rep. 14, 2494 (2024).","journal-title":"Sci. Rep."},{"key":"56545_CR19","doi-asserted-by":"crossref","unstructured":"Lenfant, M. et al. Deep learning versus iterative reconstruction for CT pulmonary angiography in the emergency setting: improved image quality and reduced radiation dose. Diagnostics (Basel Switzerland) 10(8):558 (2020).","DOI":"10.3390\/diagnostics10080558"},{"key":"56545_CR20","doi-asserted-by":"publisher","DOI":"10.1097\/RCT.0000000000001537","author":"F Catapano","year":"2023","unstructured":"Catapano, F. et al. Deep learning image reconstruction algorithm for CCTA: Image quality assessment and clinical application. J. Comput. Assist. Tomogr. https:\/\/doi.org\/10.1097\/RCT.0000000000001537 (2023).","journal-title":"J. Comput. Assist. Tomogr."},{"key":"56545_CR21","doi-asserted-by":"publisher","first-page":"5322","DOI":"10.1007\/s00330-019-06183-y","volume":"29","author":"F Tatsugami","year":"2019","unstructured":"Tatsugami, F. et al. Deep learning\u2013based image restoration algorithm for coronary CT angiography. Eur. Radiol. 29, 5322\u20135329 (2019).","journal-title":"Eur. Radiol."},{"key":"56545_CR22","first-page":"1007","volume":"64","author":"C Otgonbaatar","year":"2023","unstructured":"Otgonbaatar, C. et al. Deep learning reconstruction allows for usage of contrast agent of lower concentration for coronary CTA than filtered back projection and hybrid iterative reconstruction. Acta Radiol. (Stockholm Sweden: 1987). 64, 1007\u20131017 (2023).","journal-title":"Acta Radiol. (Stockholm Sweden: 1987)"},{"key":"56545_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-09888-3","author":"Y Nagayama","year":"2023","unstructured":"Nagayama, Y. et al. Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography. Eur. Radiol. https:\/\/doi.org\/10.1007\/s00330-023-09888-3 (2023).","journal-title":"Eur. Radiol."},{"key":"56545_CR24","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.21037\/qims-22-186","volume":"13","author":"Y Wang","year":"2023","unstructured":"Wang, Y. et al. Improving image quality and resolution of coronary arteries in coronary computed tomography angiography by using high-definition scans and deep learning image reconstruction. Quant. Imaging Med. Surg. 13, 2933\u20132940 (2023).","journal-title":"Quant. Imaging Med. Surg."},{"key":"56545_CR25","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11112037","author":"A Heinrich","year":"2021","unstructured":"Heinrich, A. et al. Deep learning-based image reconstruction for CT angiography of the aorta. Diagnostics (Basel, Switzerland) https:\/\/doi.org\/10.3390\/diagnostics11112037 (2021).","journal-title":"Diagnostics (Basel, Switzerland)"},{"key":"56545_CR26","doi-asserted-by":"publisher","first-page":"970","DOI":"10.21037\/qims-22-639","volume":"13","author":"A Heinrich","year":"2023","unstructured":"Heinrich, A. et al. Improved image quality in transcatheter aortic valve implantation planning CT using deep learning-based image reconstruction. Quant. Imaging Med. Surg. 13, 970\u2013981 (2023).","journal-title":"Quant. Imaging Med. Surg."},{"key":"56545_CR27","doi-asserted-by":"publisher","DOI":"10.1097\/RCT.0000000000001525","author":"T Kojima","year":"2023","unstructured":"Kojima, T. et al. The feasibility of deep learning-based reconstruction for low-tube-voltage CT angiography for transcatheter aortic valve implantation. J. Comput. Assist. Tomogr. https:\/\/doi.org\/10.1097\/RCT.0000000000001525 (2023).","journal-title":"J. Comput. Assist. Tomogr."},{"key":"56545_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12051287","author":"M Lenfant","year":"2022","unstructured":"Lenfant, M. et al. Deep learning-based reconstruction vs. iterative reconstruction for quality of low-dose head-and-neck CT angiography with different tube-voltage protocols in emergency-department patients. Diagnostics (Basel, Switzerland) https:\/\/doi.org\/10.3390\/diagnostics12051287 (2022).","journal-title":"Diagnostics (Basel, Switzerland)"},{"key":"56545_CR29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0209243","volume":"13","author":"IK Park","year":"2018","unstructured":"Park, I. K. et al. Non-inferior low-dose coronary computed tomography angiography image quality with knowledge-based iterative model reconstruction for overweight patients. PLoS One 13, e0209243 (2018).","journal-title":"PLoS One"},{"key":"56545_CR30","doi-asserted-by":"crossref","unstructured":"Kim, J. E. et al. Image quality and lesion detectability with low-monoenergetic imaging: A study of low-concentration iodine contrast in hepatic multiphase CT for chronic liver disease. Tomography (Ann Arbor Mich) 11(6):66 (2025).","DOI":"10.3390\/tomography11060066"},{"key":"56545_CR31","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15233026","author":"BK Kim","year":"2025","unstructured":"Kim, B. K. et al. Low- versus high-concentration iodine contrast for hepatic multiphase CT in chronic liver disease: Image quality, lesion detectability, and iodine load reduction with modern MDCT\u2014A retrospective non-inferiority study. Diagnostics (Basel, Switzerland) https:\/\/doi.org\/10.3390\/diagnostics15233026 (2025).","journal-title":"Diagnostics (Basel, Switzerland)"},{"key":"56545_CR32","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.12120725","volume":"267","author":"S Ahn","year":"2013","unstructured":"Ahn, S., Park, S. H. & Lee, K. H. How to demonstrate similarity by using noninferiority and equivalence statistical testing in radiology research. Radiology 267, 328\u2013338 (2013).","journal-title":"Radiology"},{"key":"56545_CR33","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1148\/radiol.10100047","volume":"257","author":"PD Deak","year":"2010","unstructured":"Deak, P. D., Smal, Y. & Kalender, W. A. Multisection CT protocols: sex- and age-specific conversion factors used to determine effective dose from dose-length product. Radiology 257, 158\u2013166 (2010).","journal-title":"Radiology"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-56545-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-56545-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-56545-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T12:41:12Z","timestamp":1781008872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-56545-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,9]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["56545"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-56545-y","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,9]]},"assertion":[{"value":"3 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17849"}}