Accelerated Cardiac Cine Using Locally Low Rank and Total Variation Constraints
Xin Miao 1 , Sajan Goud Lingala 2 , Yi Guo 2 , Terrence Jao 1 , and Krishna S. Nayak 1,2
Biomedical Engineering, University of
Southern California, Los Angeles, CA, United States,
Engineering, University of Southern California, Los
Angeles, CA, United States
It is well known that dynamic MRI performance can be
improved by employing constrained reconstruction that
leverages the low rank and transform sparse properties
of the dynamic image matrix. In this study, we
investigate the combination of two powerful temporal
constraints, locally low rank (LLR) and temporal total
variation (tTV), for accelerating cardiac cine imaging.
We show that this com-bination provides better
reconstruction accuracy in highly accelerated cases with
random or Cartesian golden-angle radial sampling
patterns, compared to current state-of-art constrained
reconstruction methods such as k-t SLR.
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