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Abstract #1376

Comprehensive Analysis of FatNav Motion Parameters Estimation Accuracy in 3D Brain Images Acquired at 3T

Elisa Marchetto1,2, Kevin Murphy1,3, and Daniel Gallichan1,2
1Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom, 2School of Engineering, Cardiff University, Cardiff, United Kingdom, 3School of Physics, Cardiff University, Cardiff, United Kingdom

FatNav motion-parameter estimation relies on GRAPPA reconstruction of the highly accelerated navigator fat-volumes, which might be compromised by strong changes in the head position. Data from three MPRAGE brain images have been used to find the motion corresponding to four image quality boundaries and assess motion tolerance when FatNavs are used. Results suggests that FatNavs can compensate for a large range of motion artifacts compared to when no motion correction is applied. Better correction is expected if GRAPPA weights are updated throughout the entire duration of the scan.

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