Numerical investigation of the nonlinear attenuation and dissipation of acoustic waves in a medium containing ultrasound contrast agents
Bibliographic record
Abstract
The presence of microbubble ultrasound contrast agents (UCAs) in path of an ultrasound beam increases the attenuation of the medium. A detailed knowledge about the medium attenuation in the presence of UCAs is critical for optimizing ultrasound applications. In this study, a model incorporating the nonlinear attenuation of microbubbles is developed by deriving the complex and real part of the wave number from the Calfish model. Results showed that when UCAs are sonicated by their linear resonance frequency (fr), the effective attenuation of the medium can potentially decrease as the pressure increases, in agreement with experimental observations of the attenuation of monodisperse microbubbles. When sonicated with their pressure dependent resonance frequency (fs), the effective attenuation of the medium is smaller than the case of sonication with fr, but only below a pressure threshold (Ps). Above Ps, the effective attenuation increases abruptly (e.g. ~3 fold) and becomes significantly more than the attenuation during sonication with fr. The attenuation in the subharmonic (SH) regimes of oscillations (sonication with n*fr where n = 2, 3.) are considerably smaller compared to the cases of sonication with fr and fs (~10 times). The attenuation undergoes a sharp increase concomitant with the generation of SH oscillations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".