A Multi-Objective Optimization Framework for Compound Weights in Divergent Wave Ultrasound Imaging

Divergent wave imaging (DWI) achieves high-framerate ultrasound with a broad field of view incomparable to conventional line-by-line ultrasound techniques. However, unlike traditional focused ultrasound, which applies apodization in both transmit and receive modes, DWI restricts beamforming adjustments to the receive phase, inherently limiting its ability to suppress off-axis clutter and noise. To address this, we propose a multi-objective optimization framework using genetic algorithms to derive spatial weights for DWI transmissions. Each virtual source's contribution is modeled as a depth-dependent Gaussian function, with optimized parameters-lateral spread, axial coverage, and amplitude-to enhance resolution and contrast. The approach was validated through in silico data using convex probe, demonstrating a 25 % reduction in full-width-at-half-maximum (FWHM) and a 44 % improvement in contrast ratio (CR) compared to conventional compounding with only ten virtual sources.