{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T08:38:26Z","timestamp":1792485506261,"version":"4.5.0"},"reference-count":33,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T00:00:00Z","timestamp":1690156800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To investigate the performance of a deep learning\u2010based motion correction algorithm (MCA) at various cardiac phases of coronary computed tomography angiography (CCTA), and determine the extent to which it may allow for reliable morphological and functional evaluation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and methods<\/jats:title><jats:p>The acquired image data of 53 CCTA cases, where the patient heart rate (HR) was \u226575\u00a0bpm, were reconstructed at 0,\u00a0\u00b12,\u00a0\u00b14,\u00a0\u00b16, and\u00a0\u00b18% deviations from each optimal systolic phase, with and without the MCA, yielding a total of 954 images (53 cases \u00d7 9 phases \u00d7 2 reconstructions). The overall image quality and diagnostic confidence were graded by two radiologists using a 5\u2010point scale, with scores \u22653 being deemed clinically interpretable. Signal\u2010to\u2010noise ratio, contrast\u2010to\u2010noise ratio, vessel sharpness, and circularity were measured. The CCTA\u2010derived fractional flow reserve (CT\u2010FFR) was calculated in 38 vessels on 24 patients to identify functionally significant stenosis, using the invasive fractional flow reserve (FFR) as reference. All metrics were compared between two reconstructions at various phases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Inferior image quality was observed as the phase deviation was enlarged. However, MCA significantly improved the image quality at nonoptimal phases and the optimal phase. Coronary artery evaluation was feasible within 4% phase deviation using MCA, with interpretable overall image quality and high diagnostic confidence. With MCA, the performance of identifying functionally significant stenosis via CT\u2010FFR was increased for images at various phase deviations. However, obvious decrease in accuracy, as compared to the image at the optimal phase, was found on those with deviations\u00a0&gt;4%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The deep learning\u2010based MCA allows up to 4% phase deviation in acquiring CCTA for reliable morphological and functional evaluation on patients with high HRs.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14104","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T10:05:24Z","timestamp":1690193124000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep learning\u2010based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation"],"prefix":"10.1002","volume":"24","author":[{"given":"Xiaoling","family":"Yao","sequence":"first","affiliation":[{"name":"Department of Radiology West China Hospital of 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