Applying ultrasound beamformers to photoacoustic imaging
Bibliographic record
Abstract
While single-element ultrasound (US) transducer-based photoacoustic (PA) systems have provided stunning images, they suffer from the problem of speed: they are limited in scan speed due to inevitable motion artifacts, and limited in imaging speed due to their inability to capture multiple depth images at once. Array transducers offer advantages in electronic scanning and the ability to form an image from few excitations. However, whereas a single element transducer can produce an Aline simply by taking the envelope of the received voltage traces, array transducers depend on beamforming to provide images. In commercial array US systems, the beamforming operation has traditionally been done in hardware, offering speed in exchange for flexibility. PA array systems would benefit from being able to take advantage of commercial US systems to avoid excessive hardware design, but the beamforming presents a challenge beyond that of laser synchronization and US output suppression. Many US array systems offer the ability to adjust the speed of sound (c) to compensate for differences in the mechanical properties of different materials. Typical US beamformers are based on a second-order approximation of delays that are used to refocus the incoming US data, so the optimal choice of c is scaled by √2 rather than two as one might at first surmise. Using this second order approximation along with some image rescaling, we show that US beamformers typical in commercial systems can be adapted for use in PA imaging by adjustment of c, and some image coordinate remapping.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.002 |
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".