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Abstract #0534

Using machine learning to build a population model of tongue muscle architecture based on mDIXON and diffusion tensor imaging

Robert Lloyd1,2, Iain Ball3, and Lynne Bilston1,2
1Neuroscience Research Australia, Sydney, Australia, 2University of New South Wales, Sydney, Australia, 3Philips Australia & New Zealand, Sydney, Australia


Simulations of obstructive sleep apnoea (OSA) require detailed models of the muscle fibres within the tongue, to correctly capture the motion of the tongue. Diffusion weighted images (DWI) of the oral cavity were collected for 5 healthy controls. Fibre-orientation distributions (FOD) estimated from subject DWI, can resolve intersecting muscles in the tongue. The group averaged FOD reduced the influence of spurious peaks, which gave clearer boundaries between the intrinsic muscles of the tongue. These results may help fully automate the segmentation of the muscles within the tongue.

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