Biomedical applications of sparse hemispherical ultrasound arrays
Bibliographic record
Abstract
Sparse arrays have been employed in biomedical ultrasound for diagnostic and therapeutic purposes to reduce the technical complexities associated with dense arrays while maintaining adequate performance characteristics. Sparse arrays are particularly attractive when large apertures are desired, which is often the case for therapy to achieve high acoustic gains and tight focal volumes. Large aperture arrays provide similar benefits on receive during multichannel beamforming of passively detected acoustic emissions, which is a promising approach for guiding cavitation-mediated ultrasound therapies. Our group has designed and fabricated sparse hemispherical phased arrays for ultrasound brain imaging and therapy. Ultrasound field simulations were carried out to optimize the spatial distribution of array elements. We have employed these devices for skull computed tomography-array registration, 3D spatial mapping of microbubble activity in vivo through ex-vivo human skullcaps via noninvasive aberration correction methods, and have harnessed this spatiotemporal cavitation information to calibrate exposure levels for safe, volumetric ultrasound-induced blood-brain barrier opening. In conjunction with techniques borrowed from optical microscopy, we have demonstrated trans-skull 3D microbubble imaging beyond the diffraction limit, which may have application in ultrasound-based cerebral angiography. This talk will review our progress to date in the development and biomedical application of sparse hemispherical ultrasound arrays.
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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 | 0.001 |
| 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".