A Transmit-Receive Phased Array for Microbubble-Mediated Focused Ultrasound Brain Therapy in Small Animals
Bibliographic record
Abstract
Focused ultrasound (FUS) combined with circulating microbubbles (MBs) can be employed for non-invasive, localized agent delivery across the blood-brain barrier (BBB). Previous work has demonstrated the feasibility of clinical-scale transmit-receive phased arrays for performing transcranial therapies under MB imaging feedback. OBJECTIVE: This study aimed to design, construct, and evaluate a dual-mode phased array for MB-mediated FUS brain therapy in small animals. METHODS: = 1.16 MHz) and 128 broadband PVDF receivers within a 3D-printed scaffold. RESULTS: The transmit array's focal size at the geometric focus was 0.8 mm × 0.8 mm × 1.7 mm, with a 31 mm/27 mm (lateral/axial) steering range. The receive array's point spread function was 0.6 mm × 0.6 mm × 1.5 mm (1.16 MHz source) at the geometric focus, and sources were localized up to 30 mm/16 mm (lateral/axial) from geometric focus. The array was able to spatially map MB cloud activity in 3D throughout a vessel-mimicking phantom at sub-, ultra-, and second-harmonic frequencies. Preliminary in-vivo work demonstrated its ability to induce localized BBB permeability changes under 3D sub-harmonic MB imaging feedback in a mouse model. CONCLUSION: Small form factor transmit-receive phased arrays enable acoustic imaging-controlled FUS and MB-mediated brain therapies with high targeting precision required for rodent studies. SIGNIFICANCE: Dual-mode phased arrays dedicated for small animal use will facilitate high-throughput studies of FUS-mediated BBB permeability enhancement to explore novel therapeutic strategies for future clinical application.
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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 | 0.000 |
| 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".