Analysis of a dual aperture approach and standing wave suppression pulse sequences for controlled transvertebral focused ultrasound delivery in <i>ex vivo</i> human thoracic vertebrae
Bibliographic record
Abstract
Focused ultrasound (FUS), in conjunction with microbubbles (MB), can open the blood-brain barrier to aid therapeutic delivery. Its feasibility for blood-spinal cord barrier opening (BSCBO) has been demonstrated in small animals. Controlled transvertebral delivery of FUS to the human vertebral canal requires low frequencies to penetrate thick vertebral bone. The resulting focal depth of field (DoF) is comparable to the size of the canal, promoting the formation of standing waves (SW) and potentially compromising treatment safety. Through k-Wave simulations and experimental acoustic field scans, a confocal, dual aperture approach was investigated in combination with SW suppressing pulses (linear chirp, short bursts and random phase shift keying (PSK)) to simultaneously reduce DoF and mitigate SWs. Two transducers (470/530 kHz, angle 90°) reduced the DoF by 85% compared to a single transducer (500 kHz). While all modified pulses reduced SWs relative to a 30 cycle sinusoid, short bursts performed significantly better than longer burst methods such as linear chirps (p = 0.03, (59±15) vs (34±11)% reduction). In combination with PSK, short bursts implemented in the dual aperture configuration mitigated SWs, while producing a uniform focal spot. The feasibility of using confocal, dual-aperture FUS to create a controlled focus within ex vivo vertebrae has been demonstrated. Next steps will include investigating MB emissions under short burst, PSK exposures, and characterizing spectral content associated with BSCBO and tissue damage.
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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) | 0.001 | 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".