Keywords: Analysis/Processing, Brain, Optical position tracking
Motivation: Deep learning methods are popular for head pose tracking in many applications; however, most models focus on large motions that are not applicable to the sub-millimeter accuracy required for motion correction in magnetic resonance imaging.
Goal(s): We aim to create a deep neural network capable of “markerlessly” tracking incremental changes in head pose in 6 degrees of freedom (DOF) with sub-millimeter/degree accuracy.
Approach: We pre-trained a network on simulated images of a face in our expected environment as preparation for real-world data collection and training.
Results: Initial test results show a low average mean squared error of 0.0588 mm/degrees across the 6DOF.
Impact: A deep neural network for sub-millimeter head pose tracking for motion correction was successfully pre-trained on simulated face data, with a test mean MSE of 0.0588 mm/degrees. This method shows potential towards motion correction applications in MRI.
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