An Ultrasound Contrast Agent Microbubble in a Microvessel: A Numerical Approach
Bibliographic record
Abstract
The blood brain barrier (BBB), a selective barrier separating blood from the parenchyma of the central nervous system, restricts more than 98% of neurotherapeutics from traveling into the brain. Focused ultrasound (FUS) exposure combined with circulating microbubbles is an emerging technique capable of safely opening the BBB locally, transiently, and non-invasively, enabling targeted drug delivery in the brain. However, the mechanisms of the microbubble-vessel interactions central to this process are not fully understood. \nIn this thesis, a comprehensive numerical model of a microbubble within a microvessel was developed aiming to shed light on bubble-vessel interactions, vessel wall mechanical stresses and acoustic emissions during FUS-induced BBB opening. An upward shift in the bubble's resonance frequency relative to unbound bubbles was calculated, whose magnitude was dependent on the vessel elasticity. The synergistic effects of acoustic frequency and vessel elasticity on wall stresses were investigated. Vessel wall stresses were found to be maximal when the bubble was driven above resonance. The numerical model was validated with ex vivo high-speed optical imaging experiments. Resultant amplitudes of bubble oscillation were within 15% of corresponding experimental measurements. Vessel wall stresses calculated during bubble compression (and vascular invagination) were larger than those during bubble expansion, implying that vascular damage could occur during this phase. The acoustic emissions from the ultrasound-stimulated microbubble were calculated, and their correlation with the vessel wall stresses was investigated. The normalized second harmonic decreased as a function of pressure until reaching a minimum, "transition point", after which point it was found to increase. The second and fourth harmonics of confined bubbles at this point were larger than those of unbound bubbles. Above the transition point, stresses induced by larger bubbles increased with a steeper slope. The results presented in this thesis could help in enhancing contrast imaging strategies, understanding bubble-vessel interactions, and optimizing ultrasound pulse parameters to maximize vessel wall stresses to improve drug delivery efficacy. Furthermore, the calculated acoustic emissions could provide feedback to online monitoring techniques and enable calibration of in vivo pressures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".