# GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction
## 3 Results and Discussion  
The experiments were carried out in Python3 with the TensorFlow backend. On a workstation equipped with an NVIDIA GV100 GL graphics processor unit (GPU), the reconstruction methods were executed. PSNR and SSIM were used to evaluate the acquired reconstruction outcomes. We experimented with several filter sizes in order to determine the best filter size, and we ultimately chose the filter size that produced the greatest results on our samples of public and private data. Three distinct filter sizes, including 3 \(\times\) 3, 5 \(\times\) 5 and 7 \(\times\) 7, were used. In comparison to other filter sizes, the 3 \(\times\) 3 filters produced greater results. The experimental findings on the private brain and public knee datasets with various filter sizes are shown in Figure 6.  
The model produced excellent reconstruction results (PSNR, 37.98; SSIM, 0.97). Then, using a test dataset made up of just a few hundred images from various domains, we applied our model to it. The test results of several brain and knee image reconstruction methods on a private and public dataset are shown in Figures 7-10. As demonstrated by brain images, the findings of Directly Trained (PSNR, 35.78; SSIM, 0.95) were marginally superior to those of the Calgary Model (PSNR, 34.73; SSIM, 0.94) and Image Net (PSNR, 34.25, SSIM, 0.92), which had artifacts. Figure 4: Generator architecture. Figure 5: Transfer learning for a GAN reconstruction model proposed for under-sampled MRI reconstruction. Figure 6: The graph of the PSNR/SSIM values obtained as a result of experiments by using different filter size (\(3\times 3\), \(5\times 5\) and \(7\times 7\)) on our private brain and public dataset. Figure 7: At AF = 2, Reconstruction results on private brain images. From left to right: (i) Image Net (IN); (ii) Directly trained (DT); (iii) Calgary Model (CM); (iv) Proposed GAN; (v) ZF. In the second row: (i) Image net; (ii) Directly Trained; (iii) Calgary Model; (iv) Proposed GAN results of same slices from brain image dataset after applying transfer learning. Figure 8: At AF = 4, Reconstruction results on private brain images. From left to right: (i) Image Net: (ii) Directly trained; (iii) Calgary Model; (iv) Proposed GAN; (v) ZF. In the second row: (i) Image net; (ii) Directly Trained; (iii) Calgary Model; (iv) Proposed GAN results of same slices from brain image dataset after applying transfer learning. Figure 9: At AF = 2, Reconstruction results on knee dataset. From left to right: (i) Image Net: (ii) Directly trained; (iii) Calgary Model; (iv) Proposed GAN; (v) ZF. In the second row: (i) Image net; (ii) Directly Trained; (iii) Calgary Model; (iv) Proposed GAN results of same slices from knee image dataset after applying transfer learning. The outcomes of our proposed GAN model (PSNR, 37.98; SSIM, 0.97) were superior to those of the other models examined. Our proposed transfer learning model beat other reconstruction techniques (PSNR, 39.33; SSIM, 0.95). Due to a short training dataset, the DT image (PSNR = 36.05; SSIM = 0.95) produced artifacts in the associated brain images. The image net model (PSNR, 34.69; SSIM, 0.92) and CM image (PSNR, 35.25; SSIM, 0.95) had worse results than the other models. The results of our proposed GAN model at AF 4 were 37.73 PSNR and 0.96 SSIM. Image net (PSNR, 34.25; SSIM, 0.92), CM (PSNR, 34.59; SSIM, 0.93) and DT (PSNR, 35.38; SSIM, 0.95) at AF 4. The image net model performed the worst, the category model performed marginally better and the directly trained model performed better than both. Similarly, the performance was enhanced when transfer learning was applied to the same dataset at AF 4. On the knee dataset, the performance of our proposed GAN was also better than other models. As compared to the brain image dataset, the knee dataset had the lowest accuracy of all mentioned models at AF2 and AF 4. On the knee dataset, our proposed GAN model at AF 2 was 34.63 PSNR and 0.88 SSIM.