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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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