Monitoring cavitation activity through bone: Passive acoustic mapping for monitoring ultrasound therapy in the central nervous system
Bibliographic record
Abstract
The blood-brain barrier (BBB) and blood-spinal cord barrier (BSCB) are major impediments to the pharmacological treatment of central nervous system disorders. Focused ultrasound in combination with intravenously administered microbubbles can transiently increase the permeability of the BBB and BSCB to enable passage of therapeutics from the blood pool to the brain and spinal cord tissues. Safely controlling these treatments relies on information such as the cavitation state (i.e., stable vs. inertial), and location. Passive acoustic mapping (PAM) is a beamforming approach that does not require absolute time of flight knowledge and is therefore well suited for monitoring ultrasound therapy where therapy pulses are long, ranging from several cycles to milliseconds in length. PAM applied with large receiver arrays can enable three-dimensional monitoring of cavitation activity through the bone for non-invasive treatment monitoring. This talk will discuss the application of PAM in brain and spinal cord and the challenges posed by the skull and vertebral bony geometry, with a focus on recent in silico and experimental work in transvertebral PAM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".