Improving temporal stability of stable cavitation activity of circulating microbubbles using a closed-loop controller based on pulse-length regulation
Bibliographic record
Abstract
Stable cavitation (SC) has shown great potential for novel therapeutic applications. The spatiotemporal distribution of the SC activity of microbubbles circulating in a target region is not only correlated with the uniformity of treatment, but also with some undesirable effects. Therefore, it is important to achieve controllable and desirable SC activity in target regions for improved therapeutic efficiency and biosafety. This study proposes a closed-loop feedback controller based on pulse length (PL) regulation to improve the temporal stability of SC activity. Microbubbles circulating in a physiological flowing phantom were exposed to a 1 MHz focused transducer. The SC signals produced were initially received by another 7.5 MHz plane transducer, followed by high-speed signal acquisition and real-time processing. Based on the real-time-measured SC intensity excited by the current acoustic pulse, the proposed closed-loop feedback controller used three proportional coefficients to regulate the peak negative pressure (PNP) and PL of the next acoustic pulse during the acceleration and stable stages, respectively. The results show that the rise time and the temporal stability of the SC intensity of the microbubbles circulating in these two stages were improved significantly by the optimized proportional coefficients used in the proposed controller. Importantly, when compared with the traditional closed-loop feedback controller based on PNP regulation, the proposed closed-loop feedback controller based on PL regulation reduced the probability of a transition between stable and inertial cavitation, thus avoiding the risk of disadvantageous bioeffects in practical applications. These results demonstrate the effectiveness of the proposed PL-based closed-loop feedback controller and provide a feasible strategy for realization of controllable cavitation activity in applications.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".