Abstract #3804
Highly accelerated dynamic imaging reconstruction using low rank matrix completion and partial separability model
Jingyuan Lyu 1 , Yihang Zhou 1 , Ukash Nakarmi 1 , and Leslie Ying 1,2
1
Department of Electrical Engineering, State
University of New York at Buffalo, Buffalo, NY, United
States,
2
Department
of Biomedical Engineering, State University of New York
at Buffalo, Buffalo, NY, United States
This abstract presents a new approach to highly
accelerated dynamic MRI using partial separability (PS)
model. In data acquisition, k-space data is moderately
randomly undersampled at the center k-space navigator
locations, but highly undersampled at the outer k-space
for each temporal frame. In reconstruction, the
navigator data is reconstructed from undersampled data
using structured low-rank matrix completion. After all
the unacquired navigator data is estimated, the partial
separable model is used to obtain the entire dynamic
image series from highly undersampled data. The proposed
method has shown to achieve high quality reconstructions
with reduction factors up to 44, when the conventional
PS method fails.
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