{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T22:49:52Z","timestamp":1761778192948,"version":"build-2065373602"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100024099","name":"Philips Medical Systems","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100024099","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed radiographs (DRRs), generated from CT data, may enhance workflow efficiency by enabling faster radiological assessments. Various techniques exist for generating DRRs. We conducted a pilot study to explore which DRR technique best approaches the image quality of chest X-Rays (CXR). We quantitatively and qualitatively compared four DRR techniques. A retrospective convenience sample of 217 patients who underwent both ultra-low-dose (ULD) chest CT and CXR was used. Four DRRs were generated per ULDCT, and CheXNet, a neural network trained to detect 14 diseases, was applied to CXRs and DRRs to compute area under the curve (AUC) scores. For qualitative assessment, six radiologists rated the image quality of the four DRRs generated from six ULDCTs on a Likert scale from 1 to 6 (\u2018not diagnostic quality\u2019 to \u2018diagnostic quality\u2019) and provided feedback, which was analysed using inductive category development. CheXNet\u2019s AUC for CXRs was 0.80, while DRR techniques ranged from 0.75 to 0.82 (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&gt;\u20090.26). Radiologists rated the diagnostic quality of the DRRs between 3.0 and 3.5 on average. The SoftMip technique scored highest in both the quantitative (AUC\u2009=\u20090.82) and the qualitative (score\u2009=\u20093.5) evaluation. DRRs showed comparable disease detection performance to CXRs, suggesting non-inferiority. However, radiologists expressed concerns about DRR image quality, particularly in terms of resolution, noise, and overall look-and-feel. Addressing these limitations with advanced techniques may further align DRRs with the diagnostic standards of CXRs.\n                  <\/jats:p>","DOI":"10.1007\/s10278-025-01406-9","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T14:49:52Z","timestamp":1737989392000},"page":"3263-3270","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the Image Quality of Digitally Reconstructed Radiographs from Chest CT"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8790-7964","authenticated-orcid":false,"given":"Olivier T.","family":"Paalvast","sequence":"first","affiliation":[]},{"given":"Omar","family":"Hertgers","sequence":"additional","affiliation":[]},{"given":"Merlijn","family":"Sevenster","sequence":"additional","affiliation":[]},{"given":"Hildo J.","family":"Lamb","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"issue":"9","key":"1406_CR1","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1097\/RLI.0000000000000696","volume":"55","author":"L Schockel","year":"2020","unstructured":"Schockel, L., et al., Developments in X-Ray Contrast Media and the Potential Impact on Computed Tomography. Invest Radiol, 2020. 55(9): p. 592-597.","journal-title":"Invest Radiol"},{"issue":"1006","key":"1406_CR2","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1259\/bjr\/30125639","volume":"84","author":"CS Moore","year":"2011","unstructured":"Moore, C.S., et al., A method to produce and validate a digitally reconstructed radiograph-based computer simulation for optimisation of chest radiographs acquired with a computed radiography imaging system. Br J Radiol, 2011. 84(1006): p. 890-902.","journal-title":"Br J Radiol"},{"issue":"1017","key":"1406_CR3","doi-asserted-by":"publisher","first-page":"e630","DOI":"10.1259\/bjr\/47377285","volume":"85","author":"CS Moore","year":"2012","unstructured":"Moore, C.S., et al., Use of a digitally reconstructed radiograph-based computer simulation for the optimisation of chest radiographic techniques for computed radiography imaging systems. Br J Radiol, 2012. 85(1017): p. e630-9.","journal-title":"Br J Radiol"},{"key":"1406_CR4","doi-asserted-by":"crossref","unstructured":"Unberath, M., et al., DeepDRR \u2013 A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures, in Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018. 2018, Springer International Publishing. p. 98\u2013106.","DOI":"10.1007\/978-3-030-00937-3_12"},{"key":"1406_CR5","doi-asserted-by":"publisher","first-page":"207736","DOI":"10.1109\/ACCESS.2020.3038279","volume":"8","author":"P Zhang","year":"2020","unstructured":"Zhang, P., et al., Drr4covid: Learning Automated COVID-19 Infection Segmentation From Digitally Reconstructed Radiographs. IEEE Access, 2020. 8: p. 207736-207757.","journal-title":"IEEE Access"},{"key":"1406_CR6","doi-asserted-by":"crossref","unstructured":"Campo, M.I., J. Pascau, and R.S.J. Estepar. Emphysema quantification on simulated X-rays through deep learning techniques. in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). 2018. IEEE.","DOI":"10.1109\/ISBI.2018.8363572"},{"issue":"6","key":"1406_CR7","doi-asserted-by":"publisher","first-page":"2809","DOI":"10.1002\/mp.14142","volume":"48","author":"S Carey","year":"2021","unstructured":"Carey, S., et al., Comparison of conventional chest x ray with a novel projection technique for ultra-low dose CT. Med Phys, 2021. 48(6): p. 2809-2815.","journal-title":"Med Phys"},{"issue":"3","key":"1406_CR8","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1097\/RCT.0b013e31812e4b37","volume":"32","author":"H Meyer","year":"2008","unstructured":"Meyer, H., R. Juran, and P. Rogalla, softMip: a novel projection algorithm for ultra-low-dose computed tomography. 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CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. 2017. arXiv:1711.05225https:\/\/doi.org\/10.48550\/arXiv.1711.05225.","DOI":"10.48550\/arXiv.1711.05225"},{"issue":"3","key":"1406_CR12","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong, E.R., D.M. DeLong, and D.L. Clarke-Pearson, Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. 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Acad Radiol, 2020. 27(1): p. 106-112.","journal-title":"Acad Radiol"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01406-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01406-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01406-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T22:48:35Z","timestamp":1761778115000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01406-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,27]]},"references-count":17,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["1406"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01406-9","relation":{},"ISSN":["2948-2933"],"issn-type":[{"type":"electronic","value":"2948-2933"}],"subject":[],"published":{"date-parts":[[2025,1,27]]},"assertion":[{"value":"19 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This is an observational study using retrospective patient data. Patient consent has been waived by METC-Leiden Den Haag Delft for the use of patient data (number NL20210610001). Verbal consent was obtained from participants in this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"OP is appointed on a grant-subsided position in the TKI-LSH grant \u2018REPLAiCE\u2019 which as the Leiden University Medical Center and Royal Philips B.V. as partners. MS is employed by Royal Philips B.V. OH is appointed on a subsidised position which received funding from Royal Philips B.V. HL is a consultant to Royal Philips B.V.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}