Flash-Replenishment Passive Acoustic Mapping for Robust Monitoring of Transvertebral Focused Ultrasound
Bibliographic record
Abstract
Microbubble (MB)-mediated focused ultrasound (FUS) therapy targeting the spinal cord requires reliable, intra-operative treatment monitoring of cavitation activity within the spinal canal. Prefocal cavitation during transvertebral focused ultrasound can hinder assessment of intracanal cavitation events. Due to reflection and subsequent refocusing of sound by the vertebral bones, circulating MBs in the prelaminar space can generate strong acoustic signals that interfere with attenuated emissions from MBs in the canal, pushing the latter below the noise floor of prefocal cavitation activity in reconstructed images. By pairing transvertebral FUS sequencing with 'Flash-Replenishment' style pulse sequencing common in contrast ultrasound, we suppressed cavitation at its source in the prelaminar, pre-vertebral regions, and performed transvertebral passive acoustic mapping through ex vivo human vertebrae. We show ex vivo that, on average, interleaving 'flash' pulses during FUS sequencing can raise the canal-prefocal source strength ratio in reconstructed images by approximately 1.75 times compared to conventional pulse sequencing. We further show that the maximum pressure in the canal during 'flash' pulses is much lower than the subsequent 'therapeutic' pulses, suggesting the 'flash' pulse will have limited potential to induce bioeffects in the spinal cord. We then performed in vivo 'flash-replenishment' ultrasound in porcine dorsal musculature and determined reperfusion rates are on the order of 2-3 seconds in the largest vessels supplying the muscles after MB destruction allowing for flexibility in FUS sequence design. In summary, this work exemplifies that a spatial and temporal window of bubblefree prefocal space can provide an unobscured sightline for monitoring intracanal cavitation reliably.
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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".