# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### MRI-to-PET Translation:  
MRI scans also find applications in synthetic PET scan generation similar to SCT generation. A good-quality PET image requires a full-dose tracer, but the potential health hazards posed by radioactive exposures raise concerns about PET images' use. A 3D auto-context-based locality adaptive multimodality GAN (LAGAN) generates the superior FDG PET using the same kernel for every input modality. The locality-adaptive fusion network produces a fused image by learning convolutional kernels at different image locations. Then, these fused images are used for generator training keeping the number of parameters low while increasing the number of modalities. Contrary to the multimodality cases where convolution is performed globally, the method in [31] concentrates on locality adaptive convolution. In PET imaging, if the tracer dose is lowered, considering its negative effect on the patient, unnecessary noises and artifacts compromise the quality of the resultant image. Two networks, convolutional auto-encoder and GAN generate adaptive PET templates with the help of a C-PIB PET scan and a T1-weighted MRI sequence. These synthetic PET images are used to spatialize amyloid PET scans during Alzheimer's disease estimation [32]. In Multiple Sclerosis, demyelination occurs in the brain's white matter and the spinal cord. Sketcher-Refiner GAN predicts the PET-derived myelin content map from multimodal MRI by sketching the anatomy and physiological details and then producing the myelin content map. The model is an extension of CGAN with a 3D U-Net generator working on four MRI modalities as inputs [33]. A task-induced pyramid and attention GAN(TPAGAN) integrates pyramid convolution and attention module to create the absent PET image with corresponding MR. Three sub-networks perform the whole task: pyramid and attention generator, standard discriminator, and task-induced discriminator [34].  
Bidirectional mapping GAN (BMGAN), a 3D end-to-end network, in [35] makes use of image contexts and latent vectors to generate PET from brain MRI. The model employs the generator, the discriminator, and the encoder to fuse the semantic features of PET scans with the high-dimensional latent space. The forward mapping step during the model training allows the PET images to become encoded into the latent space. The backward mapping step enables the generator to produce PET images from the MRI and sampled latent vectors. Finally, the encoder reconstructs the input latent vector from the synthetic PET scan. A hybrid GAN (HGAN) employs a hybrid loss function to produce absent PET images taking a clue from corresponding MRI scans. A spatially-constrained Fisher representation (SCFR) network is used to derive statistical details from multimodal neuroimaging data [36]. In [37], cycleGAN generates fake FDG-PET from T1-weighted MRI in two manners; one is from three adjacent transverse slices, and the other is from 3D mini patches. Two CNNs, ScaleNet and HighRes3 DNet, and one CGAN were trained to map structural MR to nonspecific (NS) PET images [38]. Table 5 presents the summary of image translation.