# Generation of Synthetic Rat Brain MRI Scans with a 3D Enhanced Alpha Generative Adversarial Network
## 5 Conclusions  
In this work, improved alpha-GAN architectures were proposed to generate synthetic MRI scans of the rat brain. The new proposed loss functions used to train the generator and the new proposed normalisation layer applied to the discriminator enabled more realistic results on rat MRI data. The use of traditional data augmentation also helped to generate more diverse and realistic scans. Different techniques were used to evaluate the different architectures and strategies. The \(\alpha\)-WGANSigmaRat1 model performed better than the others in the quantitative metrics. However, with the help of some MRI experts, it was determined that the best qualitative model was \(\alpha\)-WGANSigmaRat2. The Turing test proved that it is possible to trick experts with moderate or low expertise, but it is very difficult to trick experts who are familiar with the dataset. Unfortunately, it was not possible to use this test to check which model generated the best scans. Brain structure segmentation also improved when synthetic scans were added to the original dataset, with the global Dice score improving from 0.8969 to 0.9141. The improvements in CSF segmentation were even more significant, from 0.7468 to 0.8232. It was also found that using synthetic data generated by the new data augmentation model produced better than traditional data augmentation. The segmentation task proved that this method is capable of improving segmentation tasks and therefore other DL tasks. This work has shown that it is possible to adapt tools developed mainly for processing human MR images to preclinical research. From a methodological point of view, it was possible to extend methods that work with 2D images to 3D images, and it was shown that it is possible to obtain improved segmentation results by adding synthetic scans to the original dataset. The use of data augmentation techniques in the context of preclinical research is interesting, particularly the ability to create larger datasets without scanning more animals, which contributes to the ethical 3R rule, i.e., the reducing part in particular. It is important to note that the use of GANs for data augmentation is not a substitute for traditional data augmentation but a complement. Both together contribute to increasing the amount of data. Conceptualization, A.F., R.M., S.M. and V.A. ; methodology, A.F. and V.A. ; software, A.F. ; validation, A.F., R.M., S.M. and V.A. ; formal analysis, A.F. and R.M. ; investigation, A.F. ; resources, R.M., S.M. and V.A. ; data curation, A.F., R.M., S.M. and V.A. ; writing--original draft preparation, A.F. ; writing--review and editing, A.F., R.M., S.M. and V.A. ; visualization, A.F. ; supervision, R.M., S.M. and V.A. ; project administration, V.A. All authors have read and agreed to the published version of the manuscript. The MRI dataset of rat brains was acquired in the context of the Sigma project with the reference FCT-ANR/NEU-OSD/0258/2012. This project was co-financed by the French public funding agency ANR (Agence Nationale pour la Recherche, APP Blanc International II 2012), the Portuguese FCT (Fundacao para a Ciencia e Tecnologia) and the Portuguese North Regional Operational Program (ON-2-O Novo Norte) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER), as well as the Projecto Estatgetgo cofunded by FCT (PEst+C/SAU/LA0026/2013) and the European Regional Development Fund COMPETE (FCOMP-01-0124-FEDER-037298). France Life Imaging is acknowledged for its support in funding the NeuroSpin platform of preclinical MRI scanners. This work of [PERSON] and [PERSON] has been supported by FCT- Fundacao para a Ciencia e a Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. All experiments were performed in accordance with the recommendations of the European Union Directive (2010/63/EU) and the French legislation (Decree no. 87/848) for the use and care of laboratory animals.