Abstract #0952

FOD-Net 2.0: End-to-end FOD enhancement for low angular diffusion acquisitions using deep learning

Xinyi Wang^1,2, Zihao Tang^1,2, Mariano Cabezas², Arkiev D’Souza², Fernando Calamante², Dongnan Liu^1,2, Michael Barnett^2,3, Sicong Tu², Weidong Cai¹, and Chenyu Wang^2,3

¹School of Computer Science, University of Sydney, Sydney, Australia, ²Brain and Mind Centre, University of Sydney, Sydney, Australia, ³Sydney Neuroimaging Analysis Centre, University of Sydney, Sydney, Australia

Synopsis

Keywords: Brain Connectivity, Tractography & Fibre Modelling, Fiber Orientation DistributionModern structural brain connectome pipelines and tractography techniques heavily rely on the quality of the diffusion weighted image acquisition (angular resolution) and the subsequent estimation of the fiber orientation distributions (FODs) for each voxel. Generating reliable connectomes from low angular single-shell acquisitions in clinical scenarios remains a challenging task. This work presents an end-to-end deep learning framework to enhance FOD estimates according to multi-shell acquisitions from low angular single-shell acquisitions to guarantee high quality tractography and connectomes within acceptable time and resources.

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