# Cycle Consistent Generative Motion Artifact Correction in Coronary Computed Tomography Angiography
## 2 Previous Works  
In addressing our problem, two prevalent deep learning methodologies are commonly employed. The first involves using neural networks to determine the underlying motion vectors of the artifacts. [PERSON] et al. [15] introduced constant linear motion in the axial plane on artifact-free CT scans to yield sets of motion-affected image patches along with their associated 2D motion vectors. Then, they trained CNNs to estimate the motion vectors. In [PERSON] et al. [16], a deep neural network is trained to estimate the motion vector field (MVF), which is then used for motion-compensated image reconstruction.  
The second methodology involves using neural networks to directly generate motion-corrected images based only on the input image appearance, without any explicit motion estimation. In [PERSON] et al. [13; 14], a CNN structure originally proposed for super-resolution [10] was used to generate artifact-removed images. In [PERSON] et al. [22], the pix2 pix [18] method, proposed for image-to-image translation, was used to improve CCTA images from raw CCTA imaging data using reconstructed CCTA images as targets. The reconstructed images were obtained from Snapshot Freeze (GE Healthcare), a commercial motion correction algorithm that leverages information from adjacent cardiac phases within a single cardiac cycle to mitigate motion artifacts. [PERSON] et al. [17] also utilized the pix2 pix method to convert motion-perturbed images into motion-free images. They used an automated phase selection algorithm, Smart Phase (GE Healthcare), to determine the optimal target phase within the R-R interval from four sets of image reconstructions. Then, similar to [PERSON] et al. [22], they employed Snapshot Freeze (GE Healthcare) to the optimal target phase to generate an image reconstruction that is assigned as the ground truth to train the pix2 pix network. Both GAN-based approaches demonstrated significantly improved performance on quantitative metrics such as circularity, Dice Similarity Coefficient (DSC), and Hausdorff Distance (HD) for GAN-generated images compared to raw images when evaluated against the reference phase.  
Unfortunately, corresponding CCTA images with and without motion artifacts cannot be acquired, since we cannot stop the heart to acquire images. Thus, special techniques must be employed to generate the training data necessary for supervised learning. In [PERSON] et al. [13; 14], neural style-transfer [23] is applied to convert images with artifacts into images without artifacts, which are considered as synthetic ground truth. Our proposed method applies a similar approach to that of [PERSON] et al. [14] for generating ground truth. It is important to note that this method differs significantly from the data procurement process utilized by [PERSON] et al. [22] and [PERSON] et al. [17]. This divergence in methodologies makes it challenging to directly compare our method with theirs. However, since they have both employed the popular generative adversarial network architecture, pix2 pix, we have trained our own model to specifically evaluate and compare its performance against the proposed model, CycleGAN.