# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### Preprocessing of Ground Truth Brain MRI (RQ 3)  
The training dataset for GAN contains brain MRIs that act as ground truth images to generate GAN-synthesized images. Preprocessing operations on these ground truth brain MRIs are crucial for the fidelity of the successive GAN performance. These tasks sharpens the image and removes the noise to enhance the qualitative and quantitative estimation [159]. Compared to machine learning techniques, the preprocessing is not  
Figure 8: Distribution of commonly used loss functions in the SLR. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Loss Function** & **Description** & **Probability—** & **Ref. No.** \\ \hline Fidelity loss & The fidelity loss factor indicates the dissimilarity between the fake and the spatial normalized image and is generally added to the discriminator loss function. & No & [115; 116] \\ Gradient difference (GD) loss & The GD loss is the gradient difference between the original and dummy images that retain the sharpness in the synthetic images. & No & [86; 88; 89] \\ Identity Loss & This loss is responsible for colors and intensities conservation. & Yes & [77] \\ Image alignment loss & It is based on normalized mutual information (NMI) and used for information fusion. & Yes & [82] \\ Mean p distance (MPD) & The lp-norm or mean p distance (MPD) measures the distance between synthetic and original images. & No & [25; 27] \\ Mutual information loss & Mutual Information (MI) finds the “information content” in one variable when another variable is fully observed and used as the loss function. & Yes & [16; 17; 103] \\ Multi-scale L1 loss & Multi-scale features variance between the predicted multi-channel probability map and the actual image. & No & [42; 43] \\ Registration loss & This loss penalizes the variance between the translated \& & No & [41] \\ Self-adaptive Charbonnier loss & It is the pixel-wise differences between real and fake images. & No & [140] \\ Style-transfer loss & Style-transfer loss enhances the texture and fine structure of the desired target images. & Yes & [140; 142] \\ Supervision loss & This loss, denoted by cumulative squared error, measures & No & [41] \\  & pixel shifts between original and synthetic images. & It stresses inverse consistency in the predicted transformations. & No & [41] \\ Symmetry loss & This loss balances the mean absolute error (MAE) and gradient difference (GD), indicating how the generated & No & [72] \\ Synthetic consistency loss & This loss can be imposed as a pixel-level penalty between the translated and the original image applicable to only paired datasets. & Yes & [66; 77; 83] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._exhaustive in the case of DL; still, the input image needs to undergo some treatments before feeding to the neural networks. Some regular image preprocessing steps [18; 150] are discussed below:  
* Intensity Normalization:  
MRIs from multiple centers acquired with scanners from distinct vendors and magnetic strengths show discrepancies in brightness and some induced noise [160]. To lessen these effects, intensity normalization is implemented by measuring the MR image's intensity values variability [154]. The non-uniform voxel intensities of all volumes are standardized, and then each volume is normalized to obtain the zero mean and unit standard deviation. The patient-wise batch normalization can control the overfitting. Each patient scan is normalized by dividing each sequence by its mean intensity value. The task ensures that the distribution of intensity values is preserved [149]. * Skull Stripping: Skull stripping is removing the skull from images to focus on intracranial tissues [95]. * Registration: All input images are registered to the same imaging space during the registration phase. Registration is the spatial alignment of the images to a common anatomical space [46]. Various datasets come with different statuses of registration among training images.