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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".