Towards speed-of-sound imaging with conventional ultrasound transducers using laser diode photoacoustic
Bibliographic record
Abstract
Most imaging solutions that utilize ultrasound wave propagation require the projection of temporal signals received by transducer elements into spatial maps. This process, known as beamforming, requires knowledge of the speed-of-sound (SoS) in the medium. Incorrect SoS assumptions lead to aberration artifacts, reducing image quality and limiting clinical usability. SoS is also a novel imaging biomarker for assessing tissue characteristics. In this study, we propose a spatial SoS distribution estimation method using conventional hand-held ultrasound transducers and simple laser-diode based photoacoustics. By identifying the time of flight between photoacoustic point sources and transducer elements and relating these to SoS values along corresponding propagation paths, the underlying SoS maps are reconstructed by solving an inverse problem. We validate our method through numerical simulations and ex vivo experiments. Numerical phantoms are successfully reconstructed under various noise levels and numbers of photoacoustic sources. In ex vivo experiments with chicken breast, the estimated SoS value is consistent with results reported in the literature. The proposed approach offers a low-cost, compact solution for photoacoustics-based SoS estimation in various clinical applications, such as breast and intra-operative prostate imaging, both for diagnosis and for improving image quality of acoustic-based modalities, including photoacoustic imaging.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".