Robust retrospective motion correction of head motion using navigator-based and markerless motion tracking techniques
Elisa Marchetto, Kevin Murphy, Stefan L. Glimberg, Daniel Gallichan
Abstract
Purpose
This study investigated the artifacts arising from different types of head motion in brain MR images and how well these artifacts can be compensated using retrospective correction based on two different motion-tracking techniques.
Methods
MPRAGE images were acquired using a 3 T MR scanner on a cohort of nine healthy participants. Subjects moved their head to generate circular motion (4 or 6 cycles/min), stepwise motion (small and large) and “simulated realistic” motion (nodding and slow diagonal motion), based on visual instructions. One MPRAGE scan without deliberate motion was always acquired as a “no motion” reference. Three dimensional fat-navigator (FatNavs) and a Tracoline markerless device (TracInnovations) were used to obtain motion estimates and images were separately reconstructed retrospectively from the raw data based on these different motion estimates.
Results
Image quality was recovered from both motion tracking techniques in our stepwise and slow diagonal motion scenarios in almost all cases, with the apparent visual image quality comparable to the no-motion case. FatNav-based motion correction was further improved in the case of stepwise motion using a skull masking procedure to exclude non-rigid motion of the neck from the co-registration step. In the case of circular motion, both methods struggled to correct for all motion artifacts.
Conclusion
High image quality could be recovered in cases of stepwise and slow diagonal motion using both motion estimation techniques. The circular motion scenario led to more severe image artifacts that could not be fully compensated by the retrospective motion correction techniques used.