Estimating Mean Speed-of-Sound from Sequence-Dependent Geometric Disparities

TL;DR

This paper introduces a method to estimate the mean speed-of-sound in ultrasound imaging by analyzing geometric disparities in multiple sequences, improving beamforming accuracy and tomographic reconstruction.

Contribution

It presents a novel model fitting approach leveraging sequence-dependent disparities to accurately estimate medium SoS, enhancing ultrasound image quality.

Findings

Improved tomographic SoS reconstruction accuracy by up to 63%.

Enhanced contrast-to-noise ratio in breast phantom imaging.

Effective correction of SoS mismatch in simulated and real data.

Abstract

In ultrasound beamforming, focusing time delays are typically computed with a spatially constant speed-of-sound (SoS) assumption. A mismatch between beamforming and true medium SoS then leads to aberration artifacts. Other imaging techniques such as spatially-resolved SoS reconstruction using tomographic techniques also rely on a good SoS estimate for initial beamforming. In this work, we exploit spatially-varying geometric disparities in the transmit and receive paths of multiple sequences for estimating a mean medium SoS. We use images from diverging waves beamformed with an assumed SoS, and propose a model fitting method for estimating the SoS offset. We demonstrate the effectiveness of our proposed method for tomographic SoS reconstruction. With corrected beamforming SoS, the reconstruction accuracy on simulated data was improved by 63% and 29%, respectively, for an initial SoS over- and under-estimation of 1.5%. We further demonstrate our proposed method on a breast phantom, indicating substantial improvement in contrast-to-noise ratio for local SoS mapping.