The effect of boundary proximity on the response of individual ultrasound contrast agent microbubbles
Bibliographic record
Abstract
The effect of boundary proximity on ultrasound contrast agent microbubble emissions can play an important role in the context of both targeted microbubble imaging and contrast imaging of microvascular perfusion. In this study, individual microbubbles (n = 104) were insonicated as a function of distance from either a polystyrene membrane (Opticell(TM)) or a compliant agarose boundary up to offset distances of 1000 µm by use of an optical trapping setup. An 'acoustic spectroscopy' approach was employed, which entailed transmitting a sequence of tone bursts with centre frequencies ranging from 4 to 13.5 MHz to determine the frequency and amplitude of maximum radial response (fMR and AMR, respectively). For the Opticell(TM) case, microbubble response exhibited a distinctly oscillatory pattern with increasing offset distance, with an average maximal change in peak frequency and scattered pressure amplitude of 29.6% and 73.2%, respectively, as compared to their values adjacent to the boundary. For the agarose case, microbubbles exhibited an increase in fMR and a decrease in AMR with respect to their values in free space. Simulations indicate the oscillatory dependence on Opticell(TM) distance stems from wavelength-dependent interference phenomena. A recent analytical bubble-boundary model is in broad agreement with the relative AMR changes due to the more compliant agarose layer, however underestimates the change in relative fMR at the boundary.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".