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
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
The FatNav motion correction technique
is shown to be able to correct for a large range of motion artifacts, in case
of both smoother and rougher kinds of motion. Even greater robustness is
expected by updating the GRAPPA weights throughout the scan.
Figure
5. Each coloured region in the plot bounds the rotational and
translational motion parameters range for each evaluation category
after FatNav (left) and without motion correction (right), in case of
smooth (top row) and rough motion (bottom row). FatNavs can correct
very well for an RMS value, averaged along the three axes, of
~3.7°/3mm and 2°/1.6mm for smooth and rough motion respectively
(category 4 boundary); without motion correction, image quality drops
much more quickly (~1.2°/1mm).
Figure
1. Comparison between FatNav volume before and after GRAPPA
‘re-reconstruction’, for four different amounts of motion (tables
on the left), to simulate effect of mismatched ACS data: parallel
imaging artifacts increase with the amount of motion.
