Accelerated randomized blind source suppression of quasi-static reverberant artifacts in echocardiography
Echocardiographic image sequences are frequently corrupted by quasi-static “clutter” artifacts superimposed on the dynamic myocardium. Blind Source Separation (BSS) aIgorithms are often used to suppress clutter while retaining underlying tissue. In this paper, the sparse nature of reverberant artifacts in the time frequency domain is exploited for suppression to achieve reduced computational complexity and tissue signal loss. The performance of the method was validated on five datasets simulated in Field-II and three in vivo datasets and benchmarked with respect to the Singular Value Filter (SVF) algorithm. In simulated data, the proposed method yields a normalized average error of 1.15±0.09 a.u. over five simulated datasets, compared to the SVF, which yields an error of 14.13±1.98 a.u. on the same datasets. The suppressed signal energy was also estimated to quantify loss of `Tissue signal'. The average energy of the suppressed signal for the SVF was 35.60±3.27 a.u. The average energy of the suppressed signal of the proposed method was 25.44±1.95 a.u. Incorporating sparsity and signal characteristics allowed us to accelerate the clutter suppression by three orders of magnitude in this case.