# Cycle Consistent Generative Motion Artifact Correction in Coronary Computed Tomography Angiography
## 4 Results
### Clinical Data
#### 4.2.3 Quantitative Evaluation: Motion Artifact Metrics  
Next, we extended our investigation to include the application of GAN-based methods, specifically pix2 pix and CycleGAN, to enhance the performance metrics for coronary artery imaging. Our quantitative analysis focused exclusively on normal coronary arteries, omitting diseased ones due to the challenging nature of these metrics in handling plaque.  
We employed the motion artifact metrics as proposed in [PERSON] et al. (2014), measuring isotropy, fold overlap ratio (FOR), low-intensity region score (LIRS), and motion artifact score (MAS). Isotropy, quantifying the level of motion artifacts, is evaluated based on the ratio of two eigenvalues \((\lambda_{1},\lambda_{2})\) of the vessel region's shape, aligning with methods used in [PERSON] et al. (2017). FOR, assessing the symmetry of vessel regions, and LIRS, measuring the shading effect of motion artifacts, are calculated following the methodologies outlined in [PERSON] et al. (2018).  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline  & **Method** & **Median** & **IQR** & _p-Value_ \\ \hline \multirow{3}{*}{Dice similarity coefficient} & Original & 0.488 & 0.330–0.626 & \\  & pix2 pix & 0.706 & 0.534–0.843 & \textless{}0.001 \\  & Proposed (CycleGAN) & 0.751 & 0.598–0.853 & \textless{}0.001 \\ \hline \multirow{3}{*}{Hausdorff distance} & Original & 10.440 & 8.246–13.200 & \\  & pix2 pix & 5.831 & 4.243–7.616 & \textless{}0.001 \\ \cline{1-1}  & Proposed (CycleGAN) & 5.385 & 4.123–7.000 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance Evaluation Metrics: DSC and HD.  
Figure 7: Quantitative Evaluation Using DSC and HD Metrics: Two violin plots, showcasing the performance of the pix2 pix model and the proposed (CycleGAN) model in motion artifact correction. (**a**) illustrates the distribution of Dice Similarity Coefficient (DSC) values, where CycleGAN demonstrates a higher median DSC. (**b**) displays the Hausdorff Distance (HD) metric, with CycleGAN achieving a lower median HD. Both plots include median and interquartile ranges. The “***” markers denote statistically significant differences as confirmed by the Wilcoxon signed-rank test.  
The results show that pix2 pix and CycleGAN demonstrated substantial improvements across all evaluated metrics, surpassing the performance of [PERSON] et al. [14], as detailed in Table 3. A notable increase using GAN-based methods was observed for all metrics including Isotropy, FOR, LIRS, and MAS. Furthermore, the proposed approach using CycleGAN resulted in slightly improved performance compared to pix2 pix, demonstrating the effectiveness of our method.  
We also confirmed the statistical significance of the measurements, ascertained through a Wilcoxon signed-rank test, with _p_-values smaller than 0.001. This robust statistical evidence firmly supports the superiority of pix2 pix and CycleGAN over the previous method, illustrating their marked effectiveness in reducing motion artifacts.  
For the effective calculation of these metrics, it is crucial to accurately identify specific areas, including the vessel region affected by motion, areas exhibiting low-intensity shading artifacts, and the myocardium region. To streamline this process, we implemented a seed-based region-growing technique for semi-automatic annotation of the vessel and low-intensity artifact regions. This method enhances efficiency and assists in clearly defining the segment boundaries while ensuring consistent intensity across the region. Conversely, the myocardium regions are annotated manually due to their variable appearances and shapes, which makes them less suitable for automated region growing processes.