Brain MRI Synthesis

Brain MRI synthesis focuses on generating realistic brain MRI scans using artificial intelligence, primarily to address data scarcity, privacy concerns, and the issue of missing modalities in clinical datasets. Current research employs various generative models, including diffusion models, variational autoencoders (VAEs), and image-to-image translation techniques, often incorporating physics-based constraints or uncertainty quantification to improve synthesis quality and reliability. This work is crucial for augmenting training data for improved brain tumor segmentation and other diagnostic tasks, ultimately enhancing the accuracy and accessibility of medical image analysis.