Near-Field Clutter Mitigation in Speckle Tracking Echocardiography

Area of Science:

• Ultrasound Imaging
• Biomedical Engineering
• Cardiovascular Mechanics

Background:

• Near-field (NF) clutter significantly obscures myocardial structure and dynamics in ultrasound imaging.
• Accurate myocardial motion estimation is crucial for diagnosing cardiac conditions.
• Computational efficiency and robustness are key requirements for effective clutter filtering techniques.

Purpose of the Study:

• To investigate the impact of randomized singular value decomposition (rSVD)-based NF clutter filtering on myocardial motion estimation.
• To evaluate the performance of rSVD compared to morphological component analysis (MCA) for NF clutter reduction.
• To assess the clinical feasibility of rSVD for enhancing echocardiographic image quality and motion analysis.

Main Methods:

• Simulations using finite-element models and the k-Wave toolbox to generate ultrasound images with and without phase aberration.
• Acquisition of in vivo echocardiograms from 20 healthy subjects using a coded diverging wave compounding method.
• Application of rSVD and MCA filters to radio-frequency (RF) data followed by speckle tracking; evaluation using contrast-to-noise ratios (CNRs) and root-mean-square deviations (RMSDs).

Main Results:

• In silico, rSVD-based clutter reduction demonstrated strong agreement (R²=0.95) with ground truth displacements.
• In vivo, CNR improved by 1.02 dB to 17.68 dB, with significant enhancement (∼4.9 dB) in apical segments for 80% of subjects.
• Mean RMSDs remained below 5.0% for rSVD-processed data, and rSVD proved more practical than MCA.

Conclusions:

• rSVD-based clutter filtering is reliable, accurate, and efficient for speckle tracking echocardiography.
• Matrix decomposition methods, like rSVD, are feasible for NF clutter filtering in myocardial motion estimation.
• The study confirms rSVD's potential to improve diagnostic accuracy in cardiovascular ultrasound.