Keywords: Tractography, Tractography & Fibre Modelling, Multimodal, Microscopy, structural connectivity, diffusion, machine learning
Motivation: Joint modelling of diffusion MRI and microscopy can leverage their complementary strengths to improve the estimation of fibre orientations. Ideally, these benefits would extend beyond the few datasets where dMRI and microscopy are acquired in the same brain to improve orientation estimates in in-vivo data.
Goal(s): To translate the unique properties of joint dMRI-microscopy data modelling to benefit in-vivo dMRI datasets.
Approach: We construct a domain adaptation adversarial network that can estimate microscopy-informed FODs from single-shell in-vivo dMRI.
Results: Tractography performed using network-derived FODs show improved tracking in grey matter, bottleneck regions, superficial white matter fibres, and long-range structural connectivity.
Impact: Our microscopy-informed neural network improves fibre orientation estimation from in-vivo single-shell dMRI datasets. We demonstrate improvements in fibre tracking that may enable more precise and detailed detection of connectivity, with a broad range of applications in basic and clinical neuroscience.
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