Passive acoustic mapping within the cranial vault during microbubble-mediated ultrasound brain therapy
Bibliographic record
Abstract
Over the past 15 years, the use of passive sensor arrays combined with established beamforming algorithms to image acoustic activity during ultrasound therapies, so-called passive acoustic mapping (PAM), has been increasingly investigated for cavitation-based treatment monitoring and control purposes. Our group is interested in applications of microbubble-mediated ultrasound therapy in the brain, during which the skull bone presents unique challenges for both treatment delivery and acoustic emissions monitoring. We have demonstrated that skull-specific transcranial aberration correction methods can be applied during receive beamforming to augment PAM image quality through the skull bone, borrowing techniques developed originally for transmit beam focusing. Using custom clinical-prototype transmit/receive phased arrays, we have performed 3D microbubble imaging in vivo through ex vivo human skullcaps, and have exploited the resulting spatiotemporal cavitation information for real-time exposure level calibration and offline bioeffect distribution prediction. Ultrafast processing of acoustic emissions data can uncover cavitation dynamics hidden by conventional whole-burst temporal averaging, as well as inform temporal under-sampling strategies when millisecond-long tone bursts are applied. This talk will provide a historical overview of PAM for ultrasound therapy monitoring throughout the body, followed by a summary of recent progress made in mapping cavitation activity within the cranial vault during brain applications.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".