Biaxial ultrasound driving technique for small animal blood–brain barrier opening
Bibliographic record
Abstract
Abstract Biaxial driving can more efficiently convert electrical power to forward acoustic power in piezoelectric materials, and the interaction between the orthogonal electric fields can produce a combination of extensional and shear deformations as a function of the phase difference between them to allow dynamic steering of the beam with a single-element. In this study, we demonstrate for the first time the application of a single-element biaxially driven ring transducer in vivo for blood–brain barrier opening in mice, and compare it to that achieved with a conventional single-element highly focused (F# = 0.7) spherical transducer operating at a similar frequency. Transcranial focused ultrasound (0.45 MPa, 10 ms pulse length, 1 Hz repetition frequency, 30 s duration) was applied bilaterally to mice with a 40 μl/kg bolus of DefinityTM microbubbles, employing either a single-element biaxial ring (1.482 MHz, 10 mm inner diameter, 13.75 mm outer diameter) or spherical (1.5 MHz, 35 mm diameter, F# = 0.7; RK50, FUS Instruments) transducer on each side. Follow-up MRI scans (T1 pre- and post- 0.2 mmol/kg Gd injection, T2) were acquired to assess blood–brain barrier opening volume and potential damage. Compared to blood–brain barrier opening achieved with a conventional single-element spherical focused transducer, the opening volume achieved with a single-element biaxial ring transducer was 35% smaller (p = 0.002) with a device of a ring diameter of 40% the aperture size. Axial refocusing was further demonstrated with the single-element biaxial ring transducer, yielding a 1.63 mm deeper, five-fold larger opening volume (p = 0.048) relative to its small-focus mode. The biaxial ring transducer achieved a more localized opening compared to the spherical focused transducer under the same parameters, and further enabled dynamic axial refocusing with a single-element transducer with a smaller fabrication footprint.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".