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.