# GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction
## 3 Results and Discussion

Table 2: Quantitative analysis of PSNR and SSIM values acquired from brain and knee test images using various reconstruction techniques. Figure 11: PSNR, values of reconstructed images of the knee and brain using various models, with AF = 2. Figure 12: SSIM, values of reconstructed images of the knee and brain using various models, with AF = 2. After using transfer learning, Table 3 compares the reconstruction outcomes of several models for pictures of the knee and the brain with AF = 2. Our proposed GAN models beat all other compared methods by improved PSNR (39.33) and SSIM (0.97) for brain images and PSNR (35.48) and SSIM (0.90) for knee images. The directly trained model for brain and knee image performance improved by PSNR (36.05), SSIM (0.95) and PSNR (33.05), SSIM (0.86) better than the Image Net and Calgary model. All compared models' performance increased on knee images than brain images after applying transfer learning. Figure 14: SSIM, values of reconstructed images of the knee and brain using various models, with AF = 4. Figure 13: PSNR, values of reconstructed images of the knee and brain using various models, with AF = 4. Reconstruction results for images of the knee and brain at AF = 2 after TL are shown in Figures 15 and 16. Figure 15 displays the PSNR value-based outcomes of reconstruction, while Figure 16 shows the SSIM value-based results of reconstruction after applying TL. The blue color legend depicts the brain images, and the brown color legend depicts the knee. Proposed GAN model PSNR (37.98 and 34.63), SSIM (0.97 and 0.90), on brain and knee images, respectively. Calgary model performance PSNR (35.25 and 32.79) and SSIM (0.95 and 0.84) were slightly better compared to the image net model. Directly trained model PSNR (36.05 and 33.05) and SSIM (0.95 and 0.84) results for brain and knee images, respectively. Performance increased by 3.0 % on brain images compared to knee images. The reconstruction outcomes for images of the knee and brain at AF 4 are shown in Figures 17 and 18. In comparison to the other model, the proposed GAN model demonstrated good results. Additionally, if we compare the performance between AF 2 and AF 4, the proposed GAN model enhanced PSNR and SSIM at AF 2 compared to AF 4 by 1.20 and 2% percent, respectively. Figure 16: The blue dots represent brain data SSIM values, while orange dots represent knee data SSIM values of the reconstructed images when AF = 2 following TL. Figure 17: The blue dots represent brain data PSNR values, while orange dots represent knee data PSNR values of the reconstructed images when AF = 4 following TL. Our research shows that when compared to other techniques, the reconstruction's image's distribution produced by transfer learning is more similar to the distribution of the completely sampled image, which can help with the segmentation and diagnosis of cancerous tumors. We can also successfully use transferred learning across a range of anatomies. We discovered that brain tumor samples converged faster than knee datasets. This might be because only a few transfer learning steps were necessary to achieve the best results because the brain tumor data were located at similar anatomical locations as the training data. As an alternative, we used a fixed training set and a range of iterations to test model-reconstructed image performance after fine-tuning. It makes sense to draw the conclusion that performance increases with dataset size. Given that there are essentially no data that can be collected, we think the current study is more realistic. The outcomes of reconstruction will be better than using a tiny portion of its own data for training as long as fine-tuning is carried out, regardless of whether the AF is more or less than its own under-sampling AF. A model with a low AF should be chosen for TL because both brain and knee data show that AF = 2 is ideal for fine-tuning. In the future, we will evaluate our transfer learning method's reconstruction performance compared to that of existing unsupervised learning methodologies.