Abstract #0747
Motion Compensated Dynamic Imaging without Explicit Motion Estimation
Yasir Q Mohsin 1 , Zhili Yang 2 , Sajan Goud Lingala 3 , and Mathews Jacob 4
1
Electrical Eng, University of Iowa, Iowa
City, IA, United States,
2
Electrical
Engineering, Univeristy of Rochester, NY, United States,
3
BME,
University of Iowa, IA, United States,
4
Electrical
Eng, University of Iowa, IA, United States
The focus of this abstract is to recover dynamic MRI
data from highly under-sampled measurements. Compressed
sensing schemes that exploit sparsity in Fourier and
gradient domains have enjoyed a lot of success in
breath-held cardiac MRI. However, these schemes often
result in un-acceptable spatio-temporal blurring and
residual alias artifacts, when applied to free breathing
cardiac MRI with or without cardiac gating. The main
reason is the high inter-frame motion, often introduced
by respiration. Methods that combine motion estimation
and compensation (ME-MC) have been shown to improve the
results in this context, but they come with considerably
increased computational complexity. In addition, the
joint estimation of the motion model parameters and the
signal involves a complex non-convex optimization
criterion, which is often difficult to solve. Our main
objective is to introduce a novel framework for
motion-compensated dynamic MR image recovery that does
not suffer from the above mentioned drawbacks.
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