# Pocs-Augmented CycleGAN for MR Image Reconstruction
## 5 Discussion  
We extended our previous work in integrating traditional MR image reconstruction with DL into a full paper to propose a hybrid MR image reconstruction algorithm. The new method is based on a combination of the CycleGAN and POCS. The former is a  
Figure 10: The training loss of POCS-CycleGAN trained on the brain dataset with the radial trajectory, and the sampling rate was 10%. The four curves demonstrate the loss for \(D_{y_{a}}\), \(G_{u2f},D_{y},G_{f2u}\). Figure 9: Comparison of radiologic scores between different methods over 20 subjects’ brain and knee MR images. Radiologic scores are displayed with five different colors. Vertical axis indicates the number of subjects for each radiologic score. popular DL network that has been successfully applied to many research fields [34, 35], and the latter is a standard optimization method that has been widely used in MR image reconstruction [36, 37]. While the GAN was used in MR reconstruction in [18], we used the CycleGAN because of the additional data consistency constraint enforced in it. In fact, the two mirrored projection processes of CycleGAN highly resemble the back-and-forth projections in a POCS-based data reconstruction algorithm and can be treated as a learning-based POCS process. Therefore, the proposed POCS-CycleGAN then represents a natural combination of an implicit and an explicit POCS algorithm. The performance gain of POCS-CycleGAN against CycleGAN and others was mainly due to the increase in the sample size and the reduction of artifacts in half of the training samples. Doubling the training dataset during the training process is a way to balance the training difficulty of generators and discriminators. CycleGAN did not significantly outperform compressed sensing. One reason for this could be the increase in the network parameters due to the use of two networks and subsequently needing more training data than were used in this paper. All the other assessed DL methods outperformed compressed sensing, which is consistent with the DL-based MR work published before [18, 38]. POCS-CycleGAN produced the highest SSIM, meaning a better recovery of the texture details of the images. While we only tested CycleGAN in this study, the same POCS-based augmentation process can be incorporated with other neural networks. Since POCS augmentation can be recursively applied several times, a POCS augmented network like POCS-CycleGAN may be highly valuable for applications with very limited training samples. Future work would be required to validate POCS-CycleGAN for high acceleration rates and for 3D reconstructions. Currently, POCS-CycleGAN directly learns the fully sampled images. Future work should be performed to test whether learning the artifact-dominated difference images such as those in [14] could further improve performance. Although we only tested the combination of DL and POCS, a similar idea can be tested for the combinations of DL and other traditional algorithms. For example, images reconstructed by the traditional methods can be directly treated as new training samples for successive DL-based reconstruction. Again, that would require future investigations. We only evaluated POCS as a data augmentation approach but it can be incorporated into the final image reconstruction process. For example, we can add a new POCS layer in the end to replace the k-space data of the DL reconstructed images with the acquired k-space data at the sampled k-space positions. POCS-CycleGAN was validated using MR data in a single coil. Extending it to multiple coils is straightforward by concatenating the data from all the coils during model training and image reconstruction. In other words, data from different coils will be treated as separate training samples and input. The output of the network for all coils can be easily combined to form the final image. We noted the variational network-based image reconstruction method [39] during our final manuscript preparation.