Brain Image Synthesis

Brain image synthesis uses computational methods, primarily deep learning architectures like Generative Adversarial Networks (GANs) and diffusion models, to generate realistic brain images from other data, such as different imaging modalities or even fMRI activity patterns. Current research focuses on improving the accuracy and efficiency of these techniques, particularly for handling misaligned or unpaired data across modalities (e.g., MRI to CT) and addressing challenges like data scarcity and privacy concerns through federated learning approaches. This field is crucial for advancing medical imaging, enabling the creation of synthetic datasets for training algorithms, improving diagnostic capabilities, and potentially facilitating personalized treatment strategies by extracting novel biomarkers from synthesized images.