# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### MRI-to-PET Translation:
#### 3.1.2 Image Registration  
The image registration technique processes the images to fuse them to extract more information. In some cases, moving images are transformed into fixed reference images. The purpose behind the image registration can be motion correction, pose estimation, spatial normalization, atlas-based segmentation, and aligning images from multiple subjects [39]. A deep pose estimation network speedily enables slice-to-volume and volume-to-volume registration of brain anatomy. Transformation variables are adjusted by multi-scale registrations that initiate the iterative optimization process. CGAN learns region-based distortions of multimodal registration from T1- to T2-weighted images. Regression-type CNN predicts the angle-axis depiction of 3D movement. CycleGAN can be used for cases where paired images are unavailable [40]. The cycleGAN-based model performs symmetric image registration of unimodal/multimodal images where an inverse consistency performs bi-directional spatial transformations between images. SymReg-GAN is the extension of cycleGAN that performs semi-supervised learning with labeled image pairs and neglects unlabeled pairs. The spatial transformer performs the differentiable operation and warps the moving image using an estimated transformation [41]. The geometric transformation estimates the association of physically corresponding points within a pair of images' fields-of-view (FOVs). This transformation can sometimes lead to asymmetric and biased mapping where the \"fixed\" image is unaffected and the \"moving\" image experiences an interpolation smoothing the image simultaneously. Most of the current registration methods are focused on asymmetric directional image registration. Multi-atlas-based brain image parcellation (MAP) is a technique in which numerous brain atlases are registered to a new reference map. Manually labeled brain regions are passed on and combined with the final parcellation result. The generator of multi-atlas-guided fully convolutional network (FCN) with multi-level feature skip connection (MA-FCN-SC) structure produces input brain image with parcellation [42]. In the multi-atlas guided deep learning parcellation (DLP) technique, attributes of the most suitable map led to the parcellation process of the target brain map. FCN with squeeze-and-excitation (SE) sections GAN (FCN-SE-GAN) performs better than the MAP technique since this method avoids nonlinear registration. Improvement in the result is caused by three factors: brain \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref. No.** & **GAN Model** & **Technique** \\ \hline  & & MRI-to-CT \\ \hline
[16] & GAN & MI to avoid the issue of unregistered data \\
[17] & CGAN & MI and binary cross entropy are the discriminator loss functions to achieve the task-specific goal \\
[18] & CGAN & Pixel loss penalizes pixel-wise differences between the real and SCT scan \\
[19] & CGAN & The method calculates dosimetric accuracy by SCT generation \\
[20] & MedGAN & Non-adversarial losses (combination of style loss and content loss) to obtain high- and low-frequency details of image \\
[21] & CGAN & Residual blocks are inserted into the CGAN network \\
[22] & GAN & Image-guided radiation therapy \\
[23] & AttentionGAN & The attention network helps to predict the regions of interest \\
[24] & UAGGAN & The model identifies the area of the image and puts in a suitable translation to that location \\
[25] & CycleGAN & A dense block allows better one-to-one mapping \\
[26] & CGAN & Constant image patch size. Memory requirement is independent of the image size \\ \hline  & & MRI-to-PET \\ \hline
[31] & LAGAN & Locality adaptive convolution where the same kernel for every input modality \\
[32] & GAN & Generate adaptive PET templates \\
[33] & Sketcher-Refiner GAN & Generates PET-derived myelin content map from four MRI modalities \\
[34] & TPA-GAN & Integrates pyramid convolution and attention module \\
[35] & BMGAN & Use of image contexts and latent vectors for a generation \\ \hline \hline \end{tabular}
\end{table}