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

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.