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Abstract #4443

Improved Visualization and Quantification of 4D Flow MRI Data Using Divergence-Free Wavelet Denoising

Frank Ong1, Martin Uecker1, Umar Tariq2, Albert Hsiao2, Marcus T. Alley2, Shreyas S. Vasanawala2, Michael Lustig1

1University of California, Berkeley, Berkeley, CA, United States; 2Stanford University, Palo Alto, CA, United States

A novel noise reduction processing for 4D flow MRI data using divergence-free wavelet transform is presented. Divergence-free wavelets have the advantage of enforcing soft divergence-free conditions when discretization and partial voluming result in numerical non-divergence-free components and at the same time, provide sparse representation of flow in a generally divergence-free field. Efficient denoising is achieved by appropriate shrinkage of divergence-free and non-divergence-free wavelet coefficients. To verify its performance, the proposed processing was applied on in vivo data sets and was demonstrated to improve visualization of flow data without distorting quantifications.

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Keywords

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