Hu Cheng1
GRAPPA
has been widely used in fMRI recently to improve spatial resolution. A
drawback of GRAPPA for fMRI is that head motion in the reference scans can
result in significant artifact for all the images in a run and higher
temporal noise level. This problem can be solved by TGRAPPA using time
interleaved sampling scheme. Separate reconstruction is needed for the
interleaved k-space to minimize signal variation from volume to volume caused
by phase errors. Although TGRAPPA has less statistical power than GRAPPA, the
ability of retrospective motion correction makes it appealing in some
application.