# GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction
## 1 Introduction
Number of papers are 5, 9, 13, 24, 36, 29, 48, 79 and 133 in years 2004–2020. applied. If the reader is interested in learning more about how the Sensitivity Encoder (SENSE) works, we suggest [19]. For example, the GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA), developed in 2002 by [PERSON] et al., works on complex frequency domain signals before the IFT. For an overview of these approaches, as well as a hybrid approach that incorporates the benefits of Sense and Grappa, see [20]. The Compressed Sensing (CS) approach [21] provides efficient acquisition and reconstruction of the signal with fewer samples than the Nyquist-Shannon sampling theorem limit when a signal has a sparse representation in a specified transform domain. By selecting a tiny portion of the k-space grid, CS is employed for MRI reconstruction [22]. The IFT of the zero-filled k-space exhibits incoherent artifacts that behave like additive random noise due to the underlying premise that the under-sampling is random. CS, despite being a popular technique today, promotes smooth rebuilding, which could lead to the loss of fine, anatomically significant textural characteristics. Additionally, a sizable amount of runtime is needed. Recently, various machine learning methods for MRI acceleration were suggested. In order to reconstruct MRI from under-sampled k-space data, [PERSON] and [PERSON] [23] suggested a dictionary-based learning strategy that takes advantage of the sparsity of overlapping image patches highlighting local structure. Using spatio-temporal patches to reconstruct dynamic MRI [24], elaborated on this concept. Both [25; 26] used compressed manifold learning based on Laplacian 75 Eigenmaps to reconstruct cardiac MRI and predict respiratory motion. A variational network (VN) was created in 2018 by [27] to reconstruct intricate multi-channel MR data. In [28], they suggested the MoDL architecture to handle the MRI reconstruction difficulty. Meanwhile, ref. [29] created PI-CNN, which combines parallel imaging with CNN, for high-quality real-time MRI reconstruction. A method for multi-channel image reconstruction based on residual complicated convolutional neural networks was developed by [30] to expedite parallel MR imaging. Reconstructed multi-coil MR data from under-sampled data was successfully produced by [31] using a variable splitting network (VS-Net). Sensitivity encoding and generative adversarial networks (SENSE-GAN) were merged by [32] for rapid multi-channel MRI reconstruction. For the reconstruction of multi-coil MRI, ref. [33] introduced the GrappaNet architecture. The GrappaNet trained the model from beginning to end using neural networks in addition to conventional parallel imaging techniques. Dual-domain cascade U-nets were proposed by [34] for MRI reconstruction. They showed that dual-domain techniques are superior when reconstructing multi-channel data channels simultaneously. A summary of different articles regarding deep learning-based and other models for MRI reconstruction is presented in Table 1.  
Training the network parameters and achieving reliable generalization results, all of the aforementioned approaches require a significant size of the dataset. On publicly accessible datasets, the majority of earlier investigations have verified their reconstruction performances. Gathering tens of thousands of multi-channel data points for model training in clinical applications is challenging, though, because retaining raw k-space data is not a common clinical flow. The generalization of learned image reconstruction networks trained on open datasets must therefore be improved. In order to address this issue, numerous transfer learning studies have been carried out lately.