Investigating the resonance response of a system of two ultrasound-driven lipid encapsulated microbubbles confined within a viscoelastic vessel
Bibliographic record
Abstract
Lipid encapsulated microbubbles, strongly characterized by the initial phospholipid concentration, are clinical ultrasound imaging agents. These microbubbles are commonly used as an ultrasound contrast agent and are being developed for therapeutic applications. In this work, we investigate the vibration dynamics of two microbubbles confined within a viscoelastic vessel. We developed a finite element model to study radial microbubble dynamics in a two-bubble system, as typical clinical doses result in closely spaced bubbles. Specifically, we study the effect of the vessel wall viscosity on the resonance behaviour of microbubbles in the frequency range of 2– 8 MHz. For two identical microbubbles, we observed a decrease in 50% in resonance amplitude as we increased the vessel viscosity from 0.1 to 1 Pa s as well as an 8% shift of peak resonance activity towards higher frequencies. Furthermore, we investigated the same system consisting of microbubbles with a lower initial phospholipid concentration and showed the same decrease in amplitude; however, the direction of shift of peak resonance was towards lower frequencies by 4%. Our work suggests that microbubble resonance behaviour is greatly affected by the vessel viscosity and that the extent and direction of bubble dynamics changes are dependent on shell characteristics.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".