Closed-Loop Low-Rank Echocardiographic Artifact Removal

Echocardiographic image sequences are frequently corrupted by quasi-static artifacts (“clutter”) superimposed on the moving myocardium. Conventionally, localized blind source separation methods exploiting local correlation in the clutter have proven effective in the suppression of these artifacts. These methods use the spectral characteristics to distinguish the clutter from tissue and background noise and are applied exhaustively over the data set. The exhaustive application results in high computational complexity and a loss of useful tissue signal. In this article, we develop a closed-loop algorithm in which the clutter is first detected using an adaptively determined weighting function and then removed using low-rank estimation methods. We show that our method is adaptable to different low-rank estimators, by presenting two such estimators: sparse coding in the principal component domain and nuclear norm minimization. We compare the performance of our proposed method (CLEAR) with two methods: singular value filtering (SVF) and morphological component analysis (MCA). The performance was quantified in silico by measuring the error with respect to a known “ground truth” data set with no clutter for different combinations of moving clutter and tissue. Our method retains more tissue with a lower error of 3.88 ± 0.093 dB (sparse coding) and 3.47 ± 0.78 (nuclear norm) compared with the benchmark methods 8.5 ± 0.7 dB (SVF) and 9.3 ± 0.5 dB (MCA) particularly in instances where the rate of tissue motion and artifact motion is small (≤0.25 periods of center frequency per frame) while producing comparable clutter reduction performance. CLEAR was also validated in vivo by quantifying the tracking error over the cardiac cycle on five mouse heart data sets with synthetic clutter. CLEAR reduced the error by approximately 50%, compared with 25% for the SVF.