Equivalent time active cavitation imaging
Bibliographic record
Abstract
Abstract Rationale . Despite the development of a large number of neurologically active drugs, brain diseases are difficult to treat due to the inability of many drugs to penetrate the blood-brain barrier. High-intensity focused ultrasound (HIFU) blood-brain barrier opening in a site-specific manner could significantly expand the spectrum of available drug treatments. However, without monitoring, brain damage and off-target effects can occur during these treatments. While some methods can monitor inertial cavitation, temperature increase, or passively monitor cavitation events, to the best of our knowledge none of them can actively and spatiotemporally map the HIFU pressure field during treatment. Methods . Here we detail the development of a novel ultrasound imaging modality called equivalent time active cavitation imaging (ETACI) capable of characterizing the HIFU pressure field through stable cavitation events across the field of view with an ultrafast active imaging setup. This work introduces (1) a novel plane wave sequence whose transmit delays increase linearly with transmit events enabling the sampling of high-frequency cavitation events, and (2) an algorithm allowing the processing of the microbubble signal for pressure field mapping. The pressure measurements with our modality were first carried out in vitro for hydrophone comparison and then in vivo during blood-brain barrier opening treatment in mice. Results . This study demonstrates the capability of ETACI to spatiotemporally characterize a modulation pressure field with an active imaging setup. The resulting pressure field mapping reveals a good correlation with hydrophone measurements. Further results iareprovided experimentally in vivo with promising results. Conclusion . This proof of concept establishes the first steps towards a novel ultrasound modality for monitoring focused ultrasound blood-brain barrier opening, allowing new possibilities for a safe and precise monitoring method.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".