One shot, one SoS: A real-time, single-shot global speed of sound estimator
Bibliographic record
Abstract
• We devised the first experimentally validated single-shot global speed-of-sound (SoS) estimator for ultrasound imaging. • Our new algorithm can be readily coupled with an ultrasound imaging sequence to acquire SoS values in real-time. • Accurate SoS estimates with small mean signed difference were obtained in vitro (0.4 ± 6.5 m/s) and in vivo (3.8 ± 15.7 m/s). • This work serves well to advance the emerging use of SoS in quantitative tissue characterization. • This work also benefits other imaging processes that are influenced by SoS, such as beamforming and Doppler estimation. Speed of sound (SoS), or the propagation speed of acoustic waves through a medium, is an intrinsic property of human tissue and has emerged as a new biomarker in health diagnostics. Alas, no existing technique has practically demonstrated that the tissue SoS can be robustly measured from a single pulse-echo transmission with an imaging transducer, so incorporating SoS estimation into the ultrasound imaging pipeline remains technically challenging. In this paper, we propose a novel global SoS estimation algorithm that requires only a single steered plane wave transmission for operation. Our single-shot framework derives the SoS estimate by 1) calculating each pixel’s pre-beamformed sum of normalized autocorrelation coefficients (SNAC) derived from the time-delayed channel data ensemble for an assumed SoS; 2) constructing a loss metric that is defined as, for different SoS candidates, the negated total SNAC over different pixels; 3) finding the SoS with the minimum loss value. Our single-shot SoS estimator was implemented in real-time (50 ms processing time) on a portable ultrasound research scanner. It was tested in vitro using agar staircase phantoms (SoS range: 1508–1682 m/s) and in vivo using svelte human calves (SoS range: 1573–1589 m/s). All SoS estimates were validated with reference through-transmission measurements. Results show that our framework yielded accurate SoS estimates with a small mean signed difference (MSD) in vitro (0.4 ± 6.5 m/s) and in vivo (3.8 ± 15.7 m/s). When the framework was extended to a 10-angle multi-transmission sequence, its SoS estimation performance was further improved with a smaller MSD (0.2 ± 2.0 m/s). The advent of the proposed single-shot SoS estimator can help advance the emerging use of SoS in tissue characterization and improve other imaging processes that are influenced by SoS, such as beamforming and Doppler estimation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".