# Cycle Consistent Generative Motion Artifact Correction in Coronary Computed Tomography Angiography
## 4 Results
### Phantom Study  
Our evaluation was conducted on the CAVAREV platform [26], leveraging simulated dynamic projections from the 4D XCAT phantom, featuring contrast-enhanced coronary arteries modeled after patient data. The dataset, which focuses solely on cardiac motion, was selected for the assessment of our proposed methods. Geometry calibration was sourced from a genuine clinical angiographic C-arm system. This evaluation was chosen to facilitate a direct comparison with previous work, allowing us to benchmark the effectiveness of our method in motion correction against established techniques.  
To evaluate the proposed method using the CAVAREV platform, we generated 10 C-arm CT volumes on a \(256^{3}\) grid, ensuring an isotropic voxel size of 0.5 mm, across ten distinct heart phases targeted for reconstruction. In line with prior works referenced on the CAVAREV website [27], the volume reconstructed at the heart phase of 0.9, characterized by minimal motion, served as the target phase for cross-phase style transfer, a process detailed in Section 3.2 of our methodology. The dataset comprised mid-RCA patches collected from 25 centerline points extracted from 10 CT volumes. Given data limitations, we allocated seven volumes for training, reserving two for test purposes.  
Our method's efficacy in motion correction was quantified using the 3D metric (Q3D) as defined by CAVAREV [26], complemented by Dice similarity coefficients (DSCs) to gauge the overlap between binary images, with values ranging from zero (no overlap) to one (perfect match). The motion-corrected volumes were binarized and assessed against the ground truth, represented by the segmentation mask of the coronary artery from the volume reconstructed at a quiescent heart phase. The performance of the proposed method was evaluated using the average DSC across two test volumes. This served as the performance metric, and a comparative analysis was conducted against existing methodologies, including [PERSON] et al. [14], pix2 pix, and other methods featured on the CAVAREV website [27], as documented in Table 1. The comparison methods [28; 29] are cost-minimization-based approaches, while those in [30; 31; 32] are 2D-2D- or 3D-3D-registration-based approaches.  
According to [PERSON] et al. [14], time efficiency is critical in emergency medical scenarios. In this context, our proposed DL-based method offers a notable benefit in terms of processing speed, while the pix2 pix model shows performance on par with that of [PERSON] et al. [14]. Our proposed method also exhibits an enhanced DSC score, indicating improved performance. Figure 6 displays a test example from a phase (\(\phi=50\)) characterized by significant motion artifacts. Despite the initial poor image quality, the proposed method successfully diminishes these artifacts, showcasing its efficacy through both quantitative and qualitative improvements demonstrated on the CAVAREV dataset.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Approach 1 ** & **DSC** \\ \hline Dynamic Level Set [28] & CM & 0.691 \\ Streak-Reduced ECG-Gated FDK [30] & REG3D & 0.744 \\ Residual Motion Compensation [31] & REG2D & 0.776 \\ Motion Compensation [32] & REG2D & 0.823 \\ Spatio-temporal TV [29] & CM & 0.876 \\ [PERSON] et al. (MAC-net) [14] & DL & 0.829 \\ pix2 pix & DL & 0.830 \\ Proposed Method (CycleGAN) & DL & 0.841 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of Dice scores utilizing the CAVAREV dataset across various methods.