Effects of ultrasound pulse parameters on cavitation properties of flowing microbubbles under physiologically relevant conditions
Bibliographic record
Abstract
Acoustic cavitation from ultrasound-driven microbubbles can induce diverse bioeffects that are useful in clinical therapy. However, lack of control over the cavitation activity of flowing microbubbles results in unwanted treatment regions in the targeted tissue, which influences the therapeutic efficacy and bio-safety. The aim of this study is to understand the relationship between the ultrasound pulse parameters and cavitation properties of flowing microbubbles, including the type (and transition between types), threshold, intensity and temporal distribution of cavitation. An in vitro physiological-flow phantom was fabricated, in which the microbubbles had a constant velocity, and were sonicated to a 1-MHz focused transducer at a wide range of peak negative pressures (PNPs) (0.10-1.28 MPa), pulse repetition frequencies (PRFs) (1-200 Hz) and pulse lengths (PLs) (10-400 μs). The signals from the flowing bubbles were passively detected by another 7.5-MHz plane transducer. From detailed time- and frequency-domain analysis, we found 1). The occurrence of stable cavitation (SC) and inertial cavitation (IC) depended on PNP and PL when the PRF was below a critical value (PRF threshold) that related to the fluid velocity and PNP full width at half maximum diameter of the transducer. 2) Below the PRF threshold, the PL had no influence on the temporal distribution of SC intensity; however, above the PRF threshold, the SC properties depended on the PL because of acoustically-driven diffusion. Specifically, at shorter PLs, the SC intensity had a uniform temporal distribution and was independent of the PRF; at longer PLs, the SC intensity correlated negatively with the PRF. 3) Below the PRF threshold, the IC properties were independent of the PRF. Increasing the PRF above the PRF threshold caused the IC intensity to decrease with a non-uniform temporal distribution. These results indicate that the fluid velocity and a pulsed acoustic field influence the number and properties of the replenished bubbles into the targeted region, resulting in the change of cavitation properties. In future therapeutic applications, the physiological fluid conditions must be taken into consideration to design reasonable pulse parameters and achieve desirable cavitation properties.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".