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Abstract #1719

A Deep Learning Method for Connectome Reconstruction Using Clinical MRI Protocols

Rui Zeng1, Jinglei Lv2, He Wang3, Luping Zhou2, Michael Barnett2, Fernando Calamante2, and Chenyu Wang2
1School of Biomedical Engineering, The University of Sydney, Sydney, Australia, 2The University of Sydney, Sydney, Australia, 3Fudan University, Shanghai, China

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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