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Abstract #0394

A microscopy-trained model to predict super-resolution fibre orientations from diffusion MRI

Silei Zhu1, Karla L. Miller1, Nicola K. Dinsdale2, Saad Jbabdi1, and Amy Howard3
1Nuffield department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 2Oxford Machine Learning in NeuroImaging Lab (OMNI), Department of Computer Science, University of Oxford, Oxford, United Kingdom, 3Department of Bioengineering, Imperial College, London, United Kingdom

Synopsis

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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Keywords