# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### MRI-to-PET Translation:
#### 3.1.3 Image Super-Resolution  
The super-resolution (SR) technique converts low-resolution images to high-resolution images without compromising the scanner settings and imaging sequences. These SR methods achieve higher SNR and reduced blurriness at edges compared to conventional interpolating methods [46]. In the super-resolution process, several low-resolution images taken from slightly different viewpoints are used to predict the high-resolution version. Sufficient prior information allows better prediction parameters than actual measurements [47]. The single image super-resolution (SISR) method is vital for medical images as it helps diagnose the disease. A lesion-focused SR (LFSR) method is developed that produces seemingly more realistic SR images. In the LFSR method, a multi-scan GAN (MSGAN) produces multi-scale SR and higher-dimensional images from the lower-dimensional version [48]. Training the GAN becomes complicated when the inputs are high-resolution and high-dimensional images; therefore, information learning is divided among several GANs. First, a shaping network in unconditional super-resolution GAN (SR-GAN) is employed to pick up the three-dimensional discrepancy in the shape of adult brains. Then, a texture network is applied in conditional pix2 pix GAN to improve image slices with realistic local contrast patterns. Finally, the shape network is trained with the WGAN with Gradient Penalty (WGAN-GP) method. It is an unconditional generator that grasps the brain's three-dimensional spatial distortions [49]. In [50], authors have used the progressive upscaling method to generate true colors. The multi-path architecture of the SRGAN model takes out shallow features on multiple scales where the filter sizes are three, five, and seven instead of a single scale. The upscaled features are matched back to a high-resolution image through a reconstruction convolutional layer. Enhanced sSRGAN (ESRGAN) implements super-resolution 2D MRI slice creation where slices from three different latitudes are selected for the 2D super-resolution and later reconstructed into a three-dimensional form. The first half of the three-dimensional matrices is reconstructed from high-resolution slices with good texture features. Then, the three-dimensional slices are repaired through interpolation to obtain new brain MRI data. The VGG16 [51] is employed before activation to restore the features, solve over-brightness in SRGAN, and improve performance [52]. The work of [53] is also \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref. No.** & **GAN Model** & **Technique** \\ \hline
[40] & CGAN & Slice-to-volume registration \\
[41] & CycleGAN & Symmetric registration \\
[42] & GAN & Multi-atlas-based brain image parcellation \\
[43] & GAN & Multi-atlas-guided deep learning parcellation \\
[44] & GAN & 3D image registration \\
[45] & GAN & Transfer learning for registration \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of GAN-synthesized images used for registration. based on ESR-GAN, where two neural networks complete the super-resolution task. The first network, receiving field block (RFB)-ESRGAN, selects half the number of slices for super-resolution reconstruction and MRI rebuilding and upholds high-frequency information. The second network, the noise-based network (NESRGAN), completes the second super-resolution reconstruction task with noise and interpolated sampling, repairing the reconstructed MRI's absent values. The linear interpolation technique is involved in feature extraction and up-sampling. The neonatal brain MRI is a low anisotropic resolution scan. To increase the resolution, medical images SR using GAN (MedSRGAN) uses residual whole map attention network (RWMAN) first to interpolate and then segment [54]. Existing super-resolution methods are very scale-specific and cannot be generalized over the magnification scale.