Image-based metrics in ultrasound for estimation of global speed of sound

Accurate speed-of-sound (SoS) estimation is crucial for ultrasound image formation, yet conventional systems often rely on an assumed value for imaging. We propose to leverage conventional image analysis techniques and metrics as a novel and simple approach to estimate tissue SoS. We study eleven metrics in three categories for assessing image quality, image similarity and multi-frame variation, by testing them in numerical simulations and phantom experiments, as well as testing in an in vivo scenario. Among single-frame image quality metrics, conventional Focus and a proposed metric variation on Tenengrad present satisfactory accuracy (5-8m/s on phantoms), but only when the metrics are applied after compounding multiple frames. Differential image comparison metrics were more successful overall with errors consistently under 8m/s even applied on a single pair of frames. Mutual information and correlation metrics were found to be robust in processing relatively small image patches, making them suitable for focal estimation. We present an in vivo study on breast density classification based on SoS, to showcase clinical applicability. The studied metrics do not require access to raw channel data as they can operate on post-beamformed and/or B-mode data. These image-based methods offer a computationally efficient and data-accessible alternative to existing physics- and model-based approaches for SoS estimation.