{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T16:36:50Z","timestamp":1790181410306,"version":"4.1.0"},"reference-count":28,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["19K17250"],"award-info":[{"award-number":["19K17250"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Acta Radiol"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>To assess low-contrast areas such as plaque and coronary artery stenosis, coronary computed tomography angiography (CCTA) needs to provide images with lower noise without increasing radiation doses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a deep learning-based noise-reduction method for CCTA using four-dimensional noise reduction (4DNR) as the ground truth for supervised learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods<\/jats:title>\n                    <jats:p>\\We retrospectively collected 100 retrospective ECG-gated CCTAs. We created 4DNR images using non-rigid registration and weighted averaging three timeline CCTA volumetric data with intervals of 50 ms in the mid-diastolic phase. Our method set the original reconstructed image as the input and the 4DNR as the target image and obtained the noise-reduced image via residual learning. We evaluated the objective image quality of the original and deep learning-based noise-reduction (DLNR) images based on the image noise of the aorta and the contrast-to-noise ratio (CNR) of the coronary arteries. Further, a board-certified radiologist evaluated the blurring of several heart structures using a 5-point Likert scale subjectively and assigned a coronary artery disease reporting and data system (CAD-RADS) category independently.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>DLNR CCTAs showed 64.5% lower image noise ( P\u2009&lt;\u20090.001) and achieved a 2.9 times higher CNR of coronary arteries than that in original images, without significant blurring in subjective comparison ( P\u2009&gt;\u20090.1). The intra-observer agreement of CAD-RADS in the DLNR image was excellent (0.87, 95% confidence interval = 0.77\u20130.99) with original CCTAs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our DLNR method supervised by 4DNR significantly reduced the image noise of CCTAs without affecting the assessment of coronary stenosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/02841851221141656","type":"journal-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T02:22:03Z","timestamp":1670466123000},"page":"1831-1840","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth"],"prefix":"10.1177","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1119-1386","authenticated-orcid":false,"given":"Takuma","family":"Kobayashi","sequence":"first","affiliation":[{"name":"Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Japan"},{"name":"Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8353-2181","authenticated-orcid":false,"given":"Tatsuya","family":"Nishii","sequence":"additional","affiliation":[{"name":"Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6278-3958","authenticated-orcid":false,"given":"Kensuke","family":"Umehara","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Japan"},{"name":"Medical Informatics Section, Department of Medical Technology, QST Hospital, National Institutes for Quantum Science and Technology (QST), Chiba, Japan"},{"name":"Applied MRI Research, Department of Molecular Imaging and Theranostics, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Chiba, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junko","family":"Ota","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Japan"},{"name":"Medical Informatics Section, Department of Medical Technology, QST Hospital, National Institutes for Quantum Science and Technology (QST), Chiba, Japan"},{"name":"Applied MRI Research, Department of Molecular Imaging and Theranostics, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Chiba, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8286-0271","authenticated-orcid":false,"given":"Yasutoshi","family":"Ohta","sequence":"additional","affiliation":[{"name":"Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tetsuya","family":"Fukuda","sequence":"additional","affiliation":[{"name":"Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6851-0598","authenticated-orcid":false,"given":"Takayuki","family":"Ishida","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, 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