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Fat navigators and Moiré phase tracking comparison for motion estimation and retrospective correction
Frédéric Gretsch1, Hendrik Mattern2, Daniel Gallichan3
1Center for Biomedical Imaging, EPFL, Lausanne, Switzerland.
Magnetic Resonance in Medicine
|August 11, 2019
Summary
Fat navigators (FatNavs) and Moiré phase tracking (MPT) effectively correct motion in high-resolution MRI scans. Both methods demonstrated comparable performance, significantly improving image quality in cooperative subjects.
Area of Science:
- Magnetic Resonance Imaging (MRI)
- Medical Physics
- Image Processing
Background:
- Motion artifacts are a significant challenge in high-resolution MRI, potentially compromising diagnostic accuracy.
- Accurate motion tracking and retrospective correction are crucial for improving image quality and quantitative analysis.
Purpose of the Study:
- To compare the performance of fat navigators (FatNavs) and Moiré phase tracking (MPT) for motion tracking.
- To evaluate their efficacy in retrospective correction of very high-resolution MRI acquisitions.
Main Methods:
- Direct comparison of FatNavs and MPT motion parameters across various motion patterns.
- Acquisition of 0.5 mm isotropic MP2RAGE images with simultaneous FatNavs and MPT tracking in 9 subjects.
- Qualitative and quantitative comparison of retrospective motion corrections and assessment of FatNavs' impact on T1 maps.
Main Results:
- FatNavs and MPT showed good agreement (within 0.3 mm/° margin) in low-motion scenarios.
- Higher resolution FatNavs (2 mm) generally agreed better with MPT than 4 mm resolution.
- Retrospective corrections using MPT or FatNavs were comparable in most cases (33/36) and improved image quality.
- Potential limitations include marker attachment for MPT and temporal resolution for FatNavs.
- Navigator RF pulses caused a visible impact on T1 values distribution.
Conclusions:
- FatNavs and MPT are both excellent methods for retrospective motion correction in very high-resolution MRI.
- These techniques are particularly effective in cooperative subjects, enhancing image quality and reliability.
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