Suppression of clutter by rank adaptive reweighted sparse coding
Echocardiographic image sequences are frequently corrupted by quasi-static artifacts known as “clutter”. These artifacts are superimposed on the moving myocardium and hinder useful diagnosis. Prior work has shown the efficacy of blind source separation methods to suppress clutter while retaining underlying signal. However, the same image may be corrupted with clutter from multiple mechanisms or sources. Consequently, the spatiotemporal characteristics of the clutter vary depending on the mechanism. In this paper, a method is proposed that progressively detects and removes clutter of increasing temporal rank using sparse coding. The performance was quantified in vitro by measuring the mean squared error with respect to a ground truth dataset with no clutter. The error with respect to the ground truth was reduced from 1.3×10<sup>3</sup> ± 4.7×10<sup>2</sup> a.u. to 2.2 × 10<sup>−2</sup> ± 9.8×10<sup>−3</sup> a.u. The average clutter reduction in vivo was 11.9±0.5 dB.