# GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction
## 1 Introduction  
Magnetic Resonance Imaging (MRI) is a non-ionizing imaging technique used in biomedical research and diagnostic medicine. A strong magnetic field and Radio Frequency (RF) pulses are the foundational elements of MRI. An image is created when antennas placed near the area of the body being examined absorb hydrogen atom radiation, which is present in abundance in all living things. Due to the greater soft-tissue contrast and non-invasive nature of MRI, it is commonly utilized to identify diseases. MRI, on the other hand, has a severe problem in that it takes a long time to acquire sufficient data in k-space. In order to address this issue, k-space imaging approaches with insufficient sampling have been proposed. Compressed sensing [1] and parallel imaging [2] are two commonly used reconstruction approaches for obtaining artifact-free images. Numerous research organizations and well-known MRI scanner manufacturers are accelerating MRI acquisition. Hardware techniques such as several coils are utilized to sample k-space data in parallel [3]. One of the two main approaches is used in commercial MRI scanners [4] to reconstruct a picture from the coils' under-sampled k-space data. To be more precise, aliased pictures produced by partial k-space conversion are combined into a single coherent image via the Sensitivity Encoder (SENSE) [5]. The inverse Fouriertransform (IFT) is calibrated using GRAPPA [6], which uses information from signals in the complex frequency domain. These techniques are examined by [7], along with a hybrid approach that combines the advantages of SENSE with the GRAPPA method's resilience to some flaws. Figure 1, is a summary of data from the PubMed results for \"GPU reconstruction\" from 2004 to 2020.  
Notable is the fact that deep learning reconstructions have notably shorter reconstruction times while maintaining higher image quality [8; 9]. Using a convolutional neural network, the authors of [10] were able to determine the mapping between zero-filled (ZF) images and their corresponding fully-sampled data (CNN). Iterative processes from the ADMM algorithm were used to develop a novel deep architecture for optimizing a CS-based MRI model [11]. In [11], they recreated under-sampled 2D cardiac MR images by use of a convolutional neural network cascade. In terms of both speed and accuracy, our strategy was superior to CS approaches. De-Aliasing Generative Adversary Networks (DAGAN) was proposed by [PERSON] et al. for fast CS-MRI reconstruction in [12]. To keep perceptual image data in the generator network, an adversarial loss was combined with a unique content loss. For MRI de-aliasing [10], created a GAN with a cyclic loss. This network's reconstruction and refinement are carried out using cascaded residual U-Nets. In [13], the authors employed an L1/L2 norm and mixed-cost loss of Least Squares (LS) generator to train their deep residual network with skip connections as a generator for the reconstruction of high-quality MR images. A two-stage GAN technique, according to [14], can estimate missing k-space samples while also removing image artifacts. The self-attention technique was incorporated into a hierarchical deep residual convolutional neural network by [15] in order to improve the under-sampled MRI reconstruction. Using the self-attention mechanism and the relative average discriminator (SARAGAN), [16] constructed an artificial neural network in which half of the input data is true, and the other half is false. Research organizations and prominent MRI scanner manufacturers are working hard to speed up the acquisition of MRI scans. For example, numerous coils can be used to sample k-space data simultaneously, as demonstrated by [PERSON] and colleagues [17]. Under-sampled k-space data generated by the coils are used to reconstruct a picture in commercial MRI scanners [18]. Both approaches are now being  
Figure 1: Summary of data from the PubMed results for “GPU reconstruction” from 2004 to 2020. The number of papers in respective years is represented in the graph.