Generation of Synthetic Rat Brain MRI Scans with a 3D Enhanced Alpha Generative Adversarial Network

Featured Application The workflow can be used to train other models with other datasets. The trained model can be used to create as many synthetic MRI scans of the rat brain as required for different purposes without the need for further scanning, thus reducing animal suffering and complying with the ethical 3R rule. Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-high-field scanners are becoming available. Some disadvantages of MRI are the availability of MRI scanners and the time required for a full scanning session. Privacy laws and the 3Rs ethics rule also make it difficult to create large datasets for training deep learning models. To overcome these challenges, an adaptation of the alpha Generative Adversarial Networks (GANs) architecture was used to test its ability to generate realistic 3D MRI scans of the rat brain in silico. As far as the authors are aware, this was the first time a GAN-based approach was used to generate synthetic MRI data of the rat brain. The generated scans were evaluated using various quantitative metrics, a Turing test, and a segmentation test. The last two tests proved the realism and applicability of the generated scans to real problems. Therefore, by using the proposed new normalisation layer and loss functions, it was possible to improve the realism of the generated rat MRI scans, and it was shown that using the generated data improved the segmentation model more than using the conventional data augmentation.

Automated summary

The study successfully generated realistic MRI scans of the rat brain using an adapted Generative Adversarial Networks architecture. The generated scans were evaluated using various quantitative metrics, a Turing test, and a segmentation test, demonstrating their realism and applicability. The research also showed that using the generated data improved the segmentation model more than traditional data augmentation.