# GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction
## 3 Results and Discussion
Image net (PSNR, 32.13; SSIM, 0.81), Calgary Model (PSNR, 32.24; SSIM, 0.82) and Directly Trained (PSNR, 32.83; SSIM, 0.85) at AF 2. The Calgary model outperformed the ImageNet model, whereas the directly trained model outperformed both. The performance was enhanced when transfer learning was applied to the same dataset at AF 2. Figure 10: At AF = 4, Reconstruction results on knee dataset. Moving 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. There were the following results after applying transfer learning: Directly Trained (PSNR, 33.05; SSIM, 0.86), Image net (PSNR, 32.90; SSIM, 0.81), Calgary Model (PSNR, 32.79; SSIM, 0.84) and proposed GAN model (PSNR, 35.48: SSIM, 0.90). This study's key contribution is the development of a transfer learning enhanced GAN technique for reconstructing numerous previously unreported multi-channel MR datasets. The findings indicate that TL from our proposed method may be able to lessen variation in image contrast, anatomy and AF between the training and test datasets. With the brain tumor dataset, reconstruction image performance was better. This demonstrates that the best method might be to generate training and test data with the same contrasts because brain data were initially used to train the proposed model. When the distributions of the training and test datasets were similar, the reconstruction performance was good. The PSNR and SSIM of the images were significantly enhanced after applying transfer learning. This demonstrates that the extra information provided by these reconstructions makes fine-tuning more efficient when data are replicated across domains. Table 2 shows the comparison of different models' reconstruction results under AF = 2 for brain and knee images. The directly trained model for brain and knee images performed better than the Image Net and Calgary model. Our proposed GAN models beat all other compared methods, having PSNR (37.98) and SSIM (0.96) for brain images and PSNR (34.63) and SSIM (0.88) for knee images. All compared models perform slightly less on knee images than on brain images. Figure 11 displays the outcomes of reconstruction for knee and brain images at an AF of 2. The x-axis shows various models, and the y-axis shows the value of PSNR. The blue color legend depicts the brain images, and the brown color legend depicts the knee. The proposed GAN model had the highest accuracy (PSNR, 37.98 and 34.63) on brain and knee images, respectively. ZF and image net had the least PSNR compared to other models. Figure 12 displays the outcomes of reconstruction for knee and brain images at an AF of 2. There are multiple model counts on the x-axis, and the SSIM value is displayed on the y-axis. The knee is represented by the brown color legend, and the brain by the blue color legend. In images of the knee and the brain, the proposed GAN model had the highest SSIM (0.96 and 0.88, respectively). When compared to other models, ZF and image net had the lowest SSIM. Figures 13 and 14 show the reconstruction results at AF 4 for brain and knee images. Our proposed GAN model showed promising results as compared to the other model. Results at AF 4, PSNR and SSIM of all compared models in Figures 13 and 14 were slightly decreased. Furthermore, if we discuss the performance difference at AF 2 and AF 4, the Proposed GAN model improved PSNR by 1.06% and SSIM by 1.01% at AF 2, as compared to AF 4. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Brain** & & **Knee** \\ \hline  & PSNR & SSIM & PSNR & SSIM \\ ZF & 29.12 & 0.86 & 30.25 & 0.80 \\ ImageNet [51] & 34.25 & 0.92 & 32.13 & 0.81 \\ Directly Trained [52] & 35.78 & 0.95 & 32.83 & 0.85 \\ Calgary Model [53] & 34.73 & 0.94 & 32.24 & 0.82 \\
**Proposed GAN** & **37.98** & **0.96** & **34.63** & **0.88** \\ \hline \end{tabular}
\end{table}