A Wavefield Correlation Approach to Improve Sound Speed Estimation in Ultrasound Autofocusing

Aberration often degrades ultrasound image quality when beamforming does not account for wavefront distortions. In the past decade, local sound speed estimators have been developed for distributed aberration correction throughout a medium. Recently, iterative sound speed optimization approaches have achieved more accurate estimates than earlier approaches, but these newer methods still struggle with decreased accuracy for media with reverberation clutter and large sound speed changes. To address these challenges, we propose using a wavefield correlation (WFC) beamforming approach when performing sound speed optimization. WFC correlates simulated forward-propagated transmit wavefields and backwards-propagated receive wavefields in order to form images. This process more accurately models wave propagation in heterogeneous media and can decrease diffuse clutter due to its spatiotemporal matched filtering effect. This beamformer is implemented using auto-differentiation software to then perform gradient descent optimization, using a total-variation regularized common midpoint phase focus metric loss, on the local sound speed map used during beamforming. This approach is compared to using delay and sum (DAS) with straight-ray time delay calculations in the same sound speed optimization approach on a variety of simulated, phantom, and in vivo data with large sound speed changes and clutter. Results show that using WFC decreases sound speed estimation error, and using the estimates for aberration correction improves image resolution and contrast. These promising results have potential to improve pulse-echo imaging for challenging clinical scenarios.