Whole Brain Tractography

Whole brain tractography aims to reconstruct the brain's complex white matter pathways from diffusion MRI data, providing detailed maps of structural connectivity. Current research heavily utilizes deep learning, employing architectures like U-Nets, score-based diffusion models, and transformers, often incorporating multi-modal data (e.g., combining diffusion MRI with anatomical MRI) to improve accuracy and address challenges like incomplete field-of-view and inter-subject variability. These advancements enable more precise identification of specific tracts, improved imputation of missing data, and more accurate prediction of cognitive and clinical outcomes, impacting neuroscience research and clinical applications such as neurosurgical planning and disease diagnosis. The focus is shifting towards robust and efficient methods applicable across diverse populations and imaging protocols, particularly for challenging regions like the superficial white matter.