A High-Frequency Phased Array System for Transcranial Ultrasound Delivery in Small Animals
Bibliographic record
Abstract
Existing systems for applying transcranial focused ultrasound (FUS) in small animals produce large focal volumes relative to the size of cerebral structures available for interrogation. The use of high ultrasonic frequencies can improve targeting specificity; however, the aberrations induced by rodent calvaria at megahertz frequencies severely distort the acoustic fields produced by single-element focused transducers. Here, we present the design, fabrication, and characterization of a high-frequency phased array system for transcranial FUS delivery in small animals. A transducer array was constructed by micromachining a spherically curved PZT-5H bowl (diameter = 25 mm, radius of curvature = 20 mm, fundamental frequency = 3.3 MHz) into 64 independent elements of equal surface area. The acoustic field generated by the phased array was measured at various target locations using a calibrated fiber-optic hydrophone, both in free-field conditions as well as through ex vivo rat skullcaps with and without hydrophone-assisted phase aberration corrections. Large field-of-view acoustic field simulations were carried out to investigate potential grating lobe formation. The focal beam size obtained when targeting the array's geometric focus was [Formula: see text] mm in water. The array can steer the FUS beam electronically over cylindrical volumes of 4.5 mm in diameter and 6 mm in height without introducing grating lobes. Insertion of a rat skullcap resulted in substantial distortion of the acoustic field ( [Formula: see text]% [Formula: see text]); however, phase corrections restored partial focal quality ( [Formula: see text]% [Formula: see text]). Using phase corrections, the array is capable of generating a trans-rat skull peak negative focal pressure of up to ~2.0 MPa, which is sufficient for microbubble-mediated blood-brain barrier permeabilization at this frequency.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".