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Updated: Jul 30, 2025
Movement Retraining using Real-time Feedback of Performance
Published on: January 17, 2013
Robust retrospective motion correction of head motion using navigator-based and markerless motion tracking techniques
Elisa Marchetto1, Kevin Murphy2, Stefan L Glimberg3
1CUBRIC/School of Engineering, Cardiff University, Cardiff, UK.
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.
Insights
Retrospective motion correction effectively recovered brain MRI quality for stepwise and slow diagonal head movements. However, circular motion artifacts proved challenging to fully compensate with current techniques.
Area of Science:
- Medical Imaging
- Neuroimaging
- Magnetic Resonance Imaging
Background:
- Head motion during brain MRI acquisition introduces artifacts that degrade image quality.
- Accurate motion estimation and correction are crucial for reliable neuroimaging analysis.
Purpose of the Study:
- To investigate artifacts from various head motions in brain MRI.
- To evaluate the effectiveness of retrospective correction using two motion-tracking techniques.
Main Methods:
- MPRAGE images acquired on a 3T scanner from nine healthy participants.
- Simulated head motions: circular, stepwise, nodding, and slow diagonal.
- Motion estimation using 3D fat-navigator (FatNavs) and a markerless Tracoline device.
- Retrospective image reconstruction based on motion estimates.
Main Results:
- High image quality was recovered for stepwise and slow diagonal motions, comparable to no-motion scans.
- FatNav-based correction improved for stepwise motion with skull masking.
- Circular motion resulted in severe artifacts that were not fully compensated by either technique.
Conclusions:
- Retrospective motion correction is effective for stepwise and slow diagonal head motions in brain MRI.
- Circular motion remains a challenge for current retrospective correction methods.
- Motion-tracking techniques show promise but require further refinement for complex motion patterns.
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