Pressure estimation of ultra-high frequency ultrasound using gas vesicles
Bibliographic record
Abstract
Acoustic microscopy uses ultra-high frequency (UHF) ultrasound transducers over 80 MHz to perform high-resolution imaging. The pressure output of these transducers is unknown, as commercial calibrated hydrophones can measure pressure for transducers with frequencies only up to 80 MHz. This study used gas vesicle nanostructures (GVs) that collapse at 571 kPa to estimate the pressure of UHF transducers at 40, 80, 200, and 375 MHz. Agarose phantoms containing GVs were made, and a baseline ultrasound image was performed at low pressure to prevent GV collapse. Sections within the phantom were scanned at varying voltage to determine the GV collapse threshold. The pressure at full driving voltage was then calculated, assuming a linear relation between transducer voltage and pressure. The pressure calculated for the 40 MHz transducer was 2.2 ± 0.1 MPa at 21 °C. Using a hydrophone, the measured pressure was 2.1 ± 0.3 MPa, a difference of <2%, validating the method at this frequency. The pressure calculated for the other transducers was 2.0 ± 0.1 MPa (80 MHz), 1.2 ± 0.1 (200 MHz), and 1.05 ± 0.17 (375 MHz at 37 °C). This study addresses the challenge of estimating pressure output from UHF ultrasound transducers, demonstrating that the pressure output in the 40-400 MHz frequency range can be quantified.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".