# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### Quality Improvement:  
High-resolution images are generated from down-sampled data during the MRI analysis to save the scan time. High-resolution images in one contrast improve the quality of down-sampled images in another contrast. The anatomical details of different contrast images refine the reconstruction quality of the image. This increase in image contrast is used for the classification of brain tumors [80]. The intensity distributions of pixels in brain MR images overlap in regions of interest (ROIs) that cause low tissue contrast and create problems in accurate tissue segmentation. The cycleGAN-based model increases the contrast within the tissue by using an attention mechanism. A multistage architecture focuses on a single tissue preliminary and filters out the irrelevant context in every stage to increase high tissue contrast (HTC) images' resolution [81]. CycleGAN for unpaired data usually encodes the deformations and noises of various domains during synthetic image generation. The deformation invariant cycleGAN (DiCycleGAN) uses image alignment loss based on normalized mutual information (NMI) to strengthen the alignment between source and target domain data [82]. Generation of high-resolution MRI hippocampus region images from low-resolution MRI is arduous. Difficulty-aware GAN (da-GAN) is designed with dual discriminators and attention mechanisms in hippocampus regions for creating multimodality images. These HR images are deployed to improve hippocampal subfields classification accuracy compared to LR images [83]. In [84], Sequential GAN, a combination of two GANs, generates bi-modality images from common low-dimensional vectors. Sequential multimodal image production first creates images of one modality from low-dimensional vectors. These synthetic images are mapped to their counterparts in the other modality through image-to-image translation. The synthetic FLAIR images are not as realistic in terms of quality as synthetic T1-weighted and T2-weighted images. In [85], CGAN and the two parallel FCNs improve the quality of fake FLAIR images by retaining the contrast information of original FLAIR images. In [86], the proposed method discovers and learns global contrast from the label images to embed this information in the generated images. The capability of 2-way GAN coupled with global features in U-Net bypasses the need for paired ground truth. The multimodal images with a better perceptual quality improve the learning capability of the model.  
Single Network Generation:  
Unified GAN, the improved version of starGAN [87], generates multiple contrasts of MR images from a single modality. StarGAN can perform image translations among multiple domains with one generator and one discriminator. The single-input multiple-output (SIMO) model is trained on four different modalities. The network learns the details from the multimodal MR images and analogous modality markers. The generator takes an image of one modality producing a target modality image, and then performs the second task of recreating the original modality image through a synthesized one [88]. The available methods of multimodal image generation target only the missing image production between two modalities. CycleGAN and pix2 pixGAN can only create images from one modality to another; the former is used for unpaired images and the latter for paired images. Multimodality GAN (MGAN) simultaneously synthesizes three high-quality MR modalities (FLAIR, T1, and T1 ce) from one MR modality-T2. Complementary information provided by these modalities boosts tumor segmentation accuracy. The architecture extends starGAN for paired multimodality MR images, adding modality labels to pix2 pix. StarGAN brings the domain labels to cycleGAN and thus empowers a single network to translate an input image to any desired target domain for unpaired multidomain training images. Thus a single network translates a single modality T2 to any desired target modalities [89].