Numerical Simulations of the Nonlinear Interaction of a Bubble Cloud and a High Intensity Focused Ultrasound Field
Bibliographic record
Abstract
We studied the effects of a small bubble cloud located at the pre-focal area of a high-intensity focused ultrasound field. Our objective is to show that bubbles can modify the bioeffects of an ultrasound treatment in muscle tissue. We model a three-dimensional ultrasound field in an idealized configuration of real operating conditions. Simulations are performed using a combined method based on the Khokhlov-Zabolotskaya-Kuznetsov equation, describing the ultrasound propagation, and a Rayleigh-Plesset equation, modeling the bubble oscillations. The nonlinear interaction of the ultrasound field and the bubble oscillations is considered. Results with and without bubbles for different void fractions of the cloud and different acoustic powers are compared. The cloud induces scattering, nonlinear distortion, and shielding of ultrasound, which increase the mechanical index in the pre-focal zone, shift the location, reduce the size, and modify the shape of the volume of tissue of high mechanical index values, and lower the pressure at the intended focus considerably. Although some hypothesis and parameters used in the models do not fit the real HIFU situations, the simulation results suggest that the effects caused by a bubble cloud located in the pre-focal area should be considered and monitored to ensure the safety of high-intensity focused ultrasound treatments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".