Near-Field Clutter Mitigation in Speckle Tracking Echocardiography

Abstract

Objective

Near-field (NF) clutter filters are critical for unveiling true myocardial structure and dynamics. Randomized singular value decomposition (rSVD) stands out for its proven computational efficiency and robustness. This study investigates the effect of rSVD-based NF clutter filtering on myocardial motion estimation.

Methods

In silico, material points and their displacements in a homogeneous medium under uniaxial compressions (0.5% - 9% axial strains at 0.5% increments) were simulated in finite-element models. They were exported to the k-Wave toolbox for simulations of pre- and post-deformed ultrasound images with/ without a realistic phase aberrating layer in a high-contrast diverging wave compounding scheme. In vivo, echocardiograms of 20 normal human hearts were acquired using a coded diverging wave compounding imaging method at 3200 frames/second in the transthoracic apical four-chamber view. Morphological component analysis (MCA), which is also a sparse representation method but computationally intensive, was used for comparison with rSVD. Both rSVD- and MCA-based filters were applied to beamformed ultrasound radio-frequency (RF) data before cross-correlation-based speckle tracking. Contrast-to-noise ratios (CNRs) and root-mean-square deviations (RMSDs) were computed from regions of interest to evaluate NF clutter filtering performance of rSVD and MCA.

Results

In silico, 2-D displacements estimated from rSVD-based clutter-reduced image data showed strong agreement with ground truth (R² of 0.95). In vivo, CNR improvements ranged from 1.02 dB to 17.68 dB, consistently enhancing image quality across all subjects. An improvement of ∼4.9 dB in the apical segments was observed in 80% of cases. Mean RMSDs were below 5.0% for all rSVD-based NF clutter-reduced data. While both rSVD and MCA effectively filtered NF clutter, rSVD was significantly more practical.

Conclusion

Our findings confirm the reliability, accuracy, and efficiency of rSVD-based clutter filtering in speckle tracking echocardiography. This underscores the feasibility of matrix decomposition-based methods, exemplified by rSVD, in NF clutter filtering for myocardial motion estimation.