Cycle Consistent Generative Motion Artifact Correction in Coronary Computed Tomography Angiography

Abstract

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 using style transfer for synthetic ground truth creation, followed by CycleGAN network training for motion compensation. We employ Dynamic Time Warping (DTW) to align extracted image patches along the artery centerline with their corresponding motion-free phase patches, ensuring matched pixel correspondences and similar anatomical features for accuracy in subsequent processing steps. Our quantitative analysis, using metrics like the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), demonstrates CycleGAN’s superior performance in reducing motion artifacts, with improvements in image quality and clarity. An observer study using a 5-point Likert scale further validates the reduction of motion artifacts and improved visibility of coronary arteries. Additionally, we present a quantitative analysis on clinical data, affirming the correction of motion artifacts through metric-based evaluations.

Keywords

• coronary computed tomography angiography
• motion artifact correction
• deep learning
• generative adversarial networks
• Dynamic Time Warping