# Cycle Consistent Generative Motion Artifact Correction in Coronary Computed Tomography Angiography
## 5 Conclusions  
In this study, we have advanced the field of motion correction in coronary computed tomography angiography (CCTA) by proposing a CycleGAN-based framework. Building upon the foundations laid by [PERSON] et al. [14], this framework incorporates several key enhancements: (1) it focuses on 2D patches from the coronary artery for learning, (2) corresponding patches are extracted from different temporal phases in 3D volumes, capturing various degrees of motion, (3) CycleGAN is utilized for generating synthetic motion-corrected images, which serve as a superior alternative to the style transfer method used previously, (4) this approach enables the deep learning model to accurately learn motion correction from these synthetic images, and (5) during testing, motion-corrected patches are efficiently reinserted and interpolated back into the original 3D volumes. Our comprehensive evaluation, both quantitative and qualitative, underscores the effectiveness of this updated method using phantom and clinical datasets. Through quantitative metrics such as the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), CycleGAN demonstrated a remarkable improvement in motion artifact correction over the pix2 pix model. Additionally, our qualitative analysis, conducted by an experienced reader using a 5-point Likert scale, reflected significant enhancements in image quality and clarity, especially in the mid-right coronary artery (RCA) region. The observed improvements in motion artifact metrics--such as Isotropy, Fold Overlap Ratio (FOR), and Low-Intensity Region Score (LIRS)--were statistically significant, with \(p\)-values less than 0.001, further affirming the superiority of our CycleGAN-based approach. One of limitations of our study is the evaluation based on two specific datasets, which may not adequately represent the full spectrum of imaging environments and patient populations. This could restrict the broader applicability of our CycleGAN-based approach in diverse clinical settings. Additionally, the inherent complexity of deep learning models like CycleGAN raises challenges in interpretability, which could impact clinical trust and understanding. Figure 10: Comparison of axial views of a CT volume before and after applying the proposed coronary motion correction algorithm including re-insertion and volumetric interpolation. The upper row shows the original motion-affected slice and the lower shows the slice after motion correction using our algorithm. The RCA region is highlighted with green boxes. In future research, we intend to expand the evaluation of our method by including a wider variety of datasets, enhancing our understanding of its robustness and adaptability in different clinical settings. Building on this, our subsequent efforts will concentrate on enhancing the transparency and interpretability of deep learning models, with a particular focus on our CycleGAN-based framework. In conclusion, the promising results achieved with our CycleGAN-based approach open new avenues for enhancing the clinical utility and diagnostic accuracy of CCTA. These advancements have the potential to significantly impact patient care, allowing for more accurate diagnosis and assessment of coronary artery diseases. Conceptualization, methodology, and writing, A.M.S., S.J. and S.L. ; software and validation A.M.S. and S.J. ; resources, S.J., H.-J.C. and S.L. ; data curation, S.J. and H.-J.C.; supervision, project administration, and funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) through the Government of the Republic of Korea [Ministry of Science and ICT (MIST)] under Grant NRF-2021R1A2C2095452. Ethical review and approval were waived for this study as it used existing data without collecting or recording personal identification information, referring to information already in existence at the time of the study and utilized retrospectively for research purposes.