A PVDF Receiver for Acoustic Monitoring of Microbubble-Mediated Ultrasound Brain Therapy
Bibliographic record
Abstract
The real-time monitoring of spectral characteristics of microbubble (MB) acoustic emissions permits the prediction of increases in blood–brain barrier (BBB) permeability and of tissue damage in MB-mediated focused ultrasound (FUS) brain therapy. Single-element passive cavitation detectors provide limited spatial information regarding MB activity, greatly affecting the performance of acoustic control. However, an array of receivers can be used to spatially map cavitation events and thus improve treatment control. The spectral content of the acoustic emissions provides additional information that can be correlated with the bio-effects, and wideband receivers can thus provide the most complete spectral information. Here, we develop a miniature polyvinylidene fluoride (PVDF thickness = 110 μm, active area = 1.2 mm2) broadband receiver for the acoustic monitoring of MBs. The receiver has superior sensitivity (2.36–3.87 V/MPa) to those of a commercial fibre-optic hydrophone in the low megahertz frequency range (0.51–5.4 MHz). The receiver also has a wide −6 dB acceptance angle (54 degrees at 1.1 MHz and 13 degrees at 5.4 MHz) and the ability to detect subharmonic and higher harmonic MB emissions in phantoms. The overall acoustic performance of this low-cost receiver indicates its suitability for the eventual use within an array for MB monitoring and mapping in preclinical studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".