Abstract #0325
Accelerating MR Elastography with Sparse Sampling and Low-Rank Reconstruction
Curtis L Johnson 1 , Joseph L Holtrop 1,2 , Anthony G Christodoulou 1,3 , Matthew DJ McGarry 4 , John B Weaver 4,5 , Keith D Paulsen 4,5 , Zhi-Pei Liang 1,3 , John G Georgiadis 1,6 , and Bradley P Sutton 1,2
1
Beckman Institute, University of Illinois at
Urbana-Champaign, Urbana, IL, United States,
2
Bioengineering,
University of Illinois at Urbana-Champaign, Urbana, IL,
United States,
3
Electrical
and Computer Engineering, University of Illinois at
Urbana-Champaign, Urbana, IL, United States,
4
Thayer
School of Engineering, Dartmouth College, Hanover, NH,
United States,
5
Dartmouth-Hitchcock
Medical Center, Lebanon, NH, United States,
6
Mechanical
Science and Engineering, University of Illinois at
Urbana-Champaign, Urbana, IL, United States
Magnetic resonance elastography (MRE) requires the
acquisition of a large number of images with differing
gradient encoding direction, polarity, and displacement
phase offsets. However, these images share a lot of
information and can be represented through a reduced
model order. In this work we demonstrate the ability to
accelerate brain MRE acquisitions through sparse
sampling and low-rank image reconstruction. Reducing the
reconstructed model order from 48 to 10 resulted in
virtually unchanged mechanical properties, and allowed
for undersampling by factors up to 4x.
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