Cycle Consistent Generative Motion Artifact Correction in Coronary Computed Tomography Angiography

In coronary computed tomography angiography (CCTA), motion artifacts due to heartbeats can obscure coronary artery diagnoses. In this study, we introduce a cycle-consistent adversarial-network-based method for motion artifact correction in CCTA. Our methodology involves extracting image patches and...

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Bibliographic Details

Published in:	Applied Sciences
Main Authors:	Amal Muhammad Saleem, Sunghee Jung, Hyuk-Jae Chang, Soochahn Lee
Format:	Article
Language:	English
Published:	MDPI AG 2024-02-01
Subjects:	coronary computed tomography angiography motion artifact correction deep learning generative adversarial networks Dynamic Time Warping
Online Access:	https://www.mdpi.com/2076-3417/14/5/1859