# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### Applications of GAN-Synthesized Images for Brain MRI (RQ 1)
#### 3.1.1 Image Translation  
Deliberation of more than one imaging modality is recommended in finding the complete picture of the abnormalities. The main task in image-to-image translation is a reliable mapping from the source image to the synthetic image [14]. The process involves good adoption of the loss function to inculate this mapping into a high-quality translated image [15]. However, acquiring all modalities is impossible due to certain limitations, e.g., scanning time, cost, radiation dose, and patients' age. GAN is extensively used to generate CT and PET images from source brain MRIs. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Inclusion Criteria** & **Exclusion Criteria** \\ \hline \multirow{2}{*}{\(\blacksquare\)} & The original and empirical research studies. & \multirow{2}{*}{Research studies other than the English language.} \\  & The research studies published between 2017 and 2022. & \multirow{2}{*}{Research studies present in both databases.} \\  & The research studies providing the answer to any of the RQs. & \multirow{2}{*}{Research studies unavailable in their full-text form.} \\  & Research studies including search keywords in the title, abstract, or full text. & \multirow{2}{*}{Research studies with source modality images other than MRI.} \\  & Research studies from the Q1 or Q2 journal or a recent conference. & \multirow{2}{*}{Research studies with non-human brain images.} \\  & Research studies with no GAN involvement for image generation. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of inclusion and exclusion criteria. Figure 4: Applications of GAN-synthesized images for brain MRI. MRI-to-CT Translation:  
In current radiotherapy, both CT and MRI are used to diagnose brain abnormality better. On the one hand, CT images give an electron density score required for treatment planning; on the other hand, MRI is equally contributory with its superior contrast at soft brain tissues. MRI-only treatment planning can reduce the registration misalignment between CT and MRI scans, curtail the imaging cost, improve the radiotherapy accuracy, and lower the patient's vulnerability towards ionization radiation compared to the CT process possible with GAN. The GAN model can work on paired or unpaired type training images as inputs. The unpaired dataset is readily available but is hard to deal with as the mapping between input and output is unknown. Although challenging to access, the paired datasets present easy GAN development. The authors in [16] have generated synthetic CT images by using mutual information (MI) as the loss function to avoid the issue of misalignment between MRI and CT images. The study of [17] is the modification of [16], where the ConditionalGAN (CGAN) model checks the dosimetric accuracy of synthetic CT (SCT) images of patients with brain tumors with the aim of their use in MRI-only treatment planning for proton therapy. Along with MI, binary cross entropy is used as the activation/loss function in the discriminator. The study of [18] compares the similarity between SCT and the original CT, where a CGAN based on the pix2 pix architecture produces an SCT. Radiation dose calculation is a significant difficulty in MR-only workflow as it is hard to find electron density information from only MRI scans. The study [19] discusses SCT generation and evaluates dosimetry accuracy. The MedGAN in [20] uses the fusion of non-adversarial losses from recent image style transfer techniques to capture the desired target modality's high- and low-frequency details. MedGAN works at the image level in an end-to-end manner giving better performance compared to patch-wise training, which suffers from limited modeling capacity. CasNet, new generator architecture, uses encoder-decoder pairs to raise the acuteness of the resultant images by gradual refinement. The MR-based attenuation correction (MRAC) process is extensively used in PET/MR systems for photon attenuation correction. In the atlas-based MRAC method, CGAN generates photon attenuation maps from SCT.