Keywords: Motion Correction, Motion Correction, optical tracking, head pose estimation, brain
Motivation: Deep learning methods for head pose estimation may enable accurate, markerless optical tracking (OT), overcoming practical limitations of OT for motion correction in clinical MRI.
Goal(s): To compare the ability of three neural networks to track incremental changes in head pose in 6 degrees of freedom (6DOF) with sub-millimetre/sub-degree accuracy.
Approach: We generated a dataset of 20 heads in a simulated MRI environment with in-bore, dual-camera markerless OT and pre-trained the networks prior to training on a real-world dataset.
Results: The twin neural network had the lowest test loss (0.13 mm/° across all 6DOF) showing merit in the approach.
Impact: Accurate, markerless OT is feasible in simulations with two in-bore cameras and deep learning. Pre-training of a twin neural network was successful (mean RMSE = 0.13 mm/degrees) motivating additional development in the real world, towards motion correction in MRI.
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