# 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 centering 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. coronary computed tomography angiography; motion artifact correction; deep learning; generative adversarial networks; Dynamic Time Warping +
Footnote †: journal: Medical  
network (CNN) is trained to generate images without motion artifacts. In the work of [PERSON] et al. (2016), the pix2 pix ([PERSON] et al., 2017) generative adversarial network (GAN) method is used. In this paper, we present a new method for coronary motion correction based on GAN ([PERSON] et al., 2017) with cycle consistency ([PERSON] et al., 2017). Compared to previous work, we introduce a more robust methodology for mitigating motion artifacts in CCTA images. Initially, we create a dataset that includes spatially aligned 2D image patches. These 2D cross-sectional image patches are extracted along the RCA (Right Coronary Artery) centerline from 3D CT volumes at different phases within a 4D CT. Figure 1 showcases representative images from the dataset, illustrating the coronary artery's various phases throughout a complete cardiac cycle as captured by the 4D CT scans. A distinctive aspect of our approach is the application of Dynamic Time Warping (DTW) for the precise alignment of image patches. This method ensures that each patch extracted from the motion-affected phases is accurately matched with its corresponding patch from a phase with minimal motion artifacts. This alignment is crucial for maintaining the anatomical accuracy and consistency across the patches, forming a solid foundation for the subsequent motion correction process. Following this, style transfer is implemented using a patch from a phase with the least motion artifact as the target to generate synthetic ground truths for motion-affected patches. The goal is to generate a synthetic motion-corrected ground truth (GT) image patch as the fusion of the content of the source image, such as local anatomical elements, with the motion artifact characteristics of the target image. Leveraging the motion-free and motion-affected pairs, we train a CycleGAN model ([PERSON] et al., 2017) to produce realistic artifact-free patches. At the last stage, these 2D patches are reinserted into our 3D CT scan with volumetric interpolation to obtain the final motion-corrected 3D CT volume. This dual-step image generation process ensures the preservation of the original internal structures, while simultaneously maintaining a realistic shape for the RCA. At the testing phase, we assume the 3D CT scan is motion-perturbed, and the RCA centerline is annotated. Based on this, we proceed to extract 2D patches, correct them, and subsequently reintegrate them into the scan. The main contributions of our work are as follows.