Synthetic Brain MRI

Synthetic brain MRI generation uses deep learning, particularly Generative Adversarial Networks (GANs) and diffusion models, to create realistic artificial brain scans. Research focuses on improving the anatomical accuracy and visual fidelity of these synthetic images, often incorporating metadata like age and sex to enhance realism and relevance to neuroscience studies. This technology addresses critical limitations in medical imaging, such as data scarcity and privacy concerns, by augmenting existing datasets and enabling the training of more robust and accurate diagnostic and research models. The resulting improvements in model performance and data availability have significant implications for advancing neuroimaging research and clinical applications.