2018 JRNL PP Melissa W. Haskell - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
We introduce a data consistency based
retrospective motion correction method, TArgeted Motion
Estimation and Reduction (TAMER), to correct for patient
motion in Magnetic Resonance Imaging (MRI). Specifically,
a motion free image and motion trajectory are jointly estimated
by minimizing the data consistency error of a SENSE
forwardmodel including rigid-body subjectmotion. In order
to efficiently solve this large non-linear optimization problem,
we employ reduced modeling in the parallel imaging
formulation by assessing only a subset of target voxels at
each step of the motion search. With this strategy we are
able to effectively capture the tight coupling between the
image voxel values andmotion parameters.We demonstrate
in simulations TAMER’s ability to find similar search directions
compared to a fullmodel,with an average error of 22%,
vs. 73% error when using previously proposed alternating
methods. The reduced model decreased the computation
time 17× fold compared to a full image volume evaluation.
In phantom experiments, our method successfully mitigates
both translation and rotation artifacts, reducing image
RMSE compared to a motion-free gold standard from 21%
to 14% in a translating phantom, and from 17% to 10% in
a rotating phantom. Qualitative image improvements are
seen in human imaging of moving subjects compared to
conventional reconstruction. Finally, we compare in vivo
image results of our method to the state-of-the-art.