Abstract #0326
Compressed Sensing 4D Flow Reconstruction using Divergence-Free Wavelet Transform
Frank Ong 1 , Martin Uecker 1 , Umar Tariq 2 , Albert Hsiao 2 , Marcus Alley 2 , Shreyas Vasanawala 2 , and Michael Lustig 1
1
Electrical Engineering and Computer
Sciences, University of California, Berkeley, CA, United
States,
2
Radiology,
Stanford University, CA, United States
In our previous work, divergence-free wavelet transform
was shown to be effective in enforcing divergence-free
constraints in denoising 4D flow data. In this work, we
incorporate divergence-free wavelet in the compressed
sensing iterative reconstruction process and present an
accelerated 4D flow reconstruction method that is
tolerant to phase wraps. Effects of phase wraps are
reduced via phase cycle spinning, in which the phase is
rotated randomly in each iteration, thereby preventing
the need for phase unwrapping before reconstruction. The
proposed reconstruction was applied on in-vivo data and
was shown to yield better flow data from undersampled
data that follow boundary conditions while maintaining
core flow quantifications.
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