In this study, a deep learning model called FODSRM was developed for fiber orientation distribution (FOD) super-resolution, which enhances single-shell low-angular-resolution FOD computed from clinic-quality dMRI data (e.g., 32 directions b=1000) to obtain the super-resolved high-angular resolution quality that would have been produced from advanced research scanners (e.g., multi-shell HARDI data). The results demonstrate that the super-resolved FOD data generated by the proposed method can generate high-definition structural connectome from clinical acquisition protocols, even when applied to data from a protocol not included in the trained dataset.
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