Rizhong Lin

This study evaluates deep learning models for fODF estimation in infant brain MRI, finding that U-Net performs best with fewer gradient directions, while MLP and Transformer improve with more directions.

This study uses deep learning to estimate fiber orientation distribution functions (FODs) from limited diffusion MRI data in newborns and fetuses, achieving results comparable to standard methods with fewer measurements.