Keywords: Tractography, Brain Connectivity, White matter, structural connectivity, super resolution
Motivation: Microscopy provides fibre orientations at much higher resolutions than MRI. Diffusion MRI (dMRI) and microscopy in the same brain offer the opportunity to train a machine-learning model to super-resolve fibre orientation distributions (FODs).
Goal(s): To super-resolve FODs in dMRI based on paired MRI-microscopy data, using a model that can be applied to MRI where microscopy is unavailable.
Approach: We construct a microscopy-informed network that takes structural and single-shell dMRI, and outputs super-resolved FODs. Through domain adaptation, our network is applicable to both in-vivo and postmortem MRI.
Results: Our super-resolution FODs from macaque and in-vivo human MRI demonstrate detailed neuroanatomy.
Impact: We develop a microscopy-informed network that provides super-resolved FODs from single-shell dMRI, doubling the resolution. Notably, our network can be applied to in-vivo MRI where microscopy is unavailable and offers the possibility of more precise fibre tracking in widespread applications.
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