Deep learning improves the estimation of fiber orientation distribution for tractography in the human

Although diffusion MRI (dMRI) tractography can map brain connectivity non-invasively, accurate tractography in the human brain remains challenging due to inherent and technical limitations. In this study, we demonstrate a deep learning (DL) based approach for improving the estimation of fiber orientation distribution (FOD) from dMRI data. Trained with augmented whole brain tractography results from high-resolution dMRI data, the DL approach outperformed conventional FOD estimation methods in crossing fiber regions with dMRI data at spatial and angular resolutions comparable to routine clinical scans. The approach can potentially shorten the dMRI acquisition necessary for accurate tractography and connectome analysis.