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Abstract #4602

A Kalman Filtering Framework for Prospective Motion Correction

Julian Maclaren1, Oliver Speck2, Jrgen Hennig1, Maxim Zaitsev1

1Dept. of Diagnostic Radiology, Medical Physics, University Hospital Freiburg, Freiburg, Germany; 2Biomedical Magnetic Resonance, Otto-von-Guericke University, Magdeburg, Germany

Tracking head position and prospectively adjusting the imaging volume is becoming an increasingly popular means of preventing motion artefacts in MRI. However, the technique imposes stringent requirements on the accuracy of the tracking system used. For high-resolution imaging, these requirements are difficult to achieve in practice. We present a Kalman-filtering based approach that improves pose estimation and prediction and enables estimation of residual errors. This makes retrospective correction of residual motion artefacts possible. The result is a reduction in the required accuracy of the tracking system itself.