# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### Modality Translation:  
In the MRI acquisition, discrete imaging protocols result in different intensity distributions for a single imaging object. The recent data-driven techniques acquire MR images from multi-center and multi-device with multi-parameters. This fact gives rise to the need for universal and uniform datasets. All studies discussed in this section generate one or more MRI modalities from one or more available modalities. The redundant information of the multi-echo saturation recovery sequence with different echo time (TE) and inversion time (TI) generates multiple contrasts generally used as a reference to find a mutual-correction effect. In [66], the multi-task deep learning model (MTDL) synthesizes six 2D multi-contrast sequences: axial T1-weighted, T2-weighted, T1 and T2-FLAIR, short Tau inversion recovery (STIR), and proton density (PD) simultaneously. The registration-based synthesis approach is based on creating a single atlas responsible for loss in structural information of dummy multi-contrast images due to a nonlinear intensity transformation. Whereas the intensity-based method not depending on fixed geometric relationships among different anatomies and gives better synthesis results. PGAN is used for the generation when multi-contrast images are spatially registered, and CGAN is used when unregistered [67]. MultiModal GAN (MM-GAN), a variant of Pix2 Pix architecture, synthesizes the absent modality by merging the details from all available modalities [68]. MI-GAN, an amendment over MM-GAN, is a multi-input generative model that creates the missing modalities. Commonly acquired modalities are T1-weighted (T1), T1-contrast-enhanced (T1c), T2-weighted (T2), and T2-fluid-attenuant inversion recovery (FLAIR). The absent one is created from the other three available modalities [69]. The limitation of earlier methods of cross-modality generation is that they are not extendible to multiple modalities. The total M (M \(-\) 1) number of different generators will need to be trained to learn all sorts of mapping among M modalities. In addition to this, each translator can only use two out of M modalities simultaneously. The modality-agnostic encoder of a cycle-constrained CGAN draws out modality-invariant anatomical features and generates the desired modality with a conditioned decoder. A conditional autoencoder and discriminator can complete all \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref. No.** & **GAN Model** & **Technique** \\ \hline
[48] & MSGAN & Lesion-Focused SR method \\
[49] & SRGAN & Use of shaping network \\
[50] & SRGAN & Progressive upscaling method to generate true colors \\
[52] & ESGAN & Slices from 3 latitudes are used for SR \\
[53] & NESRGAN & Noise and interpolated sampling \\
[54] & MedSRGAN & Residual whole map attention to interpolate \\
[55] & GAN & Medical image arbitrary-scale super-resolution method \\
[57] & GAN & Improving resolution of through-plane slices \\
[58] & GAN & The image resolution of 1.5-T scanner is made equivalent to 3-T scanner. \\
[59] & FPGA & Use a divide-and-conquer manner with multiple subbands in the wavelet domain \\
[60] & End-to-end GAN & Uses a hierarchical structure \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of GAN-synthesized images used for super-resolution. Figure 5: Grouping of contrast enhancement methods. pair-wise translations. Once the feedforward processing on any modality label is over, the same autoencoder is reused to make a condition on the modality label of the original input for the cycle reconstruction [70]. The usual cross-modality image translation methods involving GAN models are based on paired data. Modular cycleGAN (MCGAN) performs unsupervised multimodal MRI translation from a single modality and retains the lesion information. The architecture includes encoders, decoders, and discriminators. MCGAN uses the combination of deconvolution and resize upsampling methods that avoid the checkerboard artifacts in the generated images [71].