# GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sr. No.** & **Reference** & **Methodology** & **Results** & **Future Directions** \\ \hline
1 & [35] & Reconstruction of brain MRI & Reconstruction results are derived from practical sampling schemes of accelerated brain MRIs. & Apply to a wide range of datasets with excellent fidelity to fully sample scans. \\ \hline
2 & [36] & Use of a GAN with a Cyclic & In terms of both running time and image quality, CS-MRI methods performed noticeably better than open-source MRI datasets. & The next step in the study will be to extend Refine GAN to handle dynamic MRI. \\ \hline
3 & [37] & The inverse problem was solved using a deep CNN-based optimization model. & Discriminative CNN denoiser creates a versatile, quick, and efficient image restoration framework. \\ \hline
4 & [38] & Image Reconstruction from Compressively Sensed Random Measurements Using Recon Net & Recon Net offers high-quality reconstructions of simulated and actual data for various measurement rates. \\ \hline
5 & [39] & To resolve problems with normal-convolutional sparse-view network performance is calculated. & Parallel beam X-ray CT & It is possible to address strategies for heterogeneous datasets. \\ \hline
6 & [40] & CS-based approaches, especially DLMRI, use a coordinate-descent algorithm to optimize. & CNNs were evaluated for their relevance to the MR image reconstruction challenge. & The model will directly address the coil sensitivity maps’ redundancy. \\ \hline
7 & [41] & The Primal-Dual algorithm for tomographic reconstruction has been learned. & For the Shepp–Logan phantom, they improve peak SNR by 6 dB over competing approaches. & Capable of using complex loss functions with learned reconstruction operators. \\ \hline
8 & [42] & Recurrent Neural Networks are used by RIMs to solve inverse problems. & The RIM-3 task model is competitive on all noise levels. & \multicolumn{1}{c}{} \\ \hline
9 & [43] & A pre-trained CNN model was used to augment and classify brain tumor data. & Before and after data augmentation, they outperformed the most sophisticated algorithms with 90.67 accuracies. & Weight-saving CNN fine-grained classification will use differential stochastic classification. \\ \hline
10 & [44] & Investigate the overfitting issue using a CapsNet for classifying brain tumors. & 86.56\%. Learning rate decreases with iterations. & In the future, look into how adding more layers affects classification accuracy. \\ \hline
11 & [45] & A review of medical image classification using deep learning approaches. & They explain deep learning algorithms and how they can be used for medical imaging, noting that the learning rate is proportional to the inverse of iterations. & To apply the strategies to the modalities where they are not employed, more research is needed. \\ \hline
12 & [46] & Predicted patient survival using BraTS2017 and U-NET. & With less computational time, 89.6\% accuracy was achieved. & \multicolumn{1}{c}{} \\ \hline
13 & [47] & BRaTS 2013, 2015 used CNN-based two-path architecture to separate brain tumors. & Cascaded input CNN achieved 88.2\% accuracy. & Increasing the architecture layers and data set boosted the outcomes even more. \\ \hline \end{tabular}
\end{table} 
Table 1: A summary of different articles regarding deep learning-based and other models for MRI reconstruction. To reconstruct high-quality images from under-sampled k-space data in MRI, ref. [48] created a unique deep learning approach with domain adaptability. The proposed network made use of a pre-trained network, which was then fine-tuned using a sparse set of radial MR datasets or synthetic radial MR datasets. [PERSON] et al. [49] investigated the effects of image content, sampling pattern, SNR, and image contrast on the generalizability of a pre-trained model in order to show the potential for transfer learning using the VN architecture. To test the ability of networks trained on normal pictures to generalize to T1- and T2-weighted brain images, ref. [50] suggested a transfer-learning approach.