The applicability of ultrasound dynamic receive beamformers to photoacoustic imaging
Bibliographic record
Abstract
Ultrasound array transducers offer several advantages over mechanically-scanned transducers for photoacoustic imaging, including high imaging frame rates and dynamic focusing. Development of a photoacoustic array system can be accelerated by adapting existing commercial ultrasound systems and harnessing their performance-enhancing aspects such as parallel beamforming. One challenge faced when adapting commercial ultrasound systems for photoacoustic imaging is that the dynamic delay sequences required for focusing must account for one-way rather than two-way ultrasound wave propagation. Modifying the hardware may be difficult for developers and impossible for users, but some ultrasound systems provide a parameter, c: the speed of sound used to calculate these delays. A linear-array based ultrasound platform with parallel channel acquisition is used to compare experimental point-spread functions produced using an ultrasound beamformer with a scaled value of c to those produced by a photoacoustic beamformer. Scaling c by a factor of √2 provides the best image quality compared with adjustments by 1 and 2, but requires image rescaling, which can be done post-acquisition or by modification of the rendering software. Although optimal focusing is achieved for linear scanning, this is not the case for sector scanning, which requires angular and depth rescaling.
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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) | 0.000 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".