Image-Based Metrics in Ultrasound for Estimation of Global Speed-of-Sound
This provides a simpler and more accessible alternative for ultrasound practitioners to improve image accuracy, though it is incremental as it builds on existing image analysis techniques.
The paper tackled the problem of estimating global speed-of-sound (SoS) in ultrasound imaging by proposing a novel approach using conventional image analysis metrics instead of complex physical models, achieving errors consistently under 8 m/s with computationally efficient methods like Mean Square Error.
Accurate speed-of-sound (SoS) estimation is crucial for ultrasound image formation, yet conventional systems often rely on an assumed value for imaging. While several methods exist for SoS estimation, they typically depend on complex physical models of acoustic propagation. 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. Among single-frame image quality metrics, conventional Focus and our proposed Smoothed Threshold Tenengrad metrics achieved satisfactory accuracy, however only when applied to compounded images. Image quality metrics were largely surpassed by various image comparison metrics, which exhibited errors consistently under 8 m/s even applied to a single pair of images. Particularly, Mean Square Error is a computationally efficient alternative for global estimation. Mutual Information and Correlation are found to be robust to processing small image segments, making them suitable, e.g., for multi-layer SoS estimation. The above metrics do not require access to raw channel data as they can operate on post-beamformed data, and in the case of image quality metrics they can operate on B-mode images, given that the beamforming SoS can be controlled for beamforming using a multitude of values. These image analysis based SoS estimation methods offer a computationally efficient and data-accessible alternative to conventional physics-based methods, with potential extensions to layered or local SoS imaging.