Transcranial acoustic imaging for real-time control of ultrasound-mediated blood-brain barrier opening using a clinical-scale prototype system
Bibliographic record
Abstract
Multichannel beamforming of passively detected ultrasound (US)-stimulated acoustic emissions is a promising method for guiding cavitation-mediated therapies. In the context of brain applications, our group and others have previously demonstrated the use of conventional beamforming techniques to transcranially map cavitation activity during microbubble (MB)-mediated blood-brain barrier (BBB) opening. MB activity can be mapped at pressure levels below the BBB opening threshold, allowing target confirmation prior to therapy delivery. By including skull-specific phase and amplitude corrections in the reconstruction process, the aberrating effects of the cranial bone can be compensated for to improve image quality. Recently, we have designed, fabricated, and characterized multi-frequency, transmit/receive, sparse hemispherical phased arrays for MB-mediated brain therapy and simultaneous cavitation mapping [Deng et al., Phys. Med. Biol. 61, 8476-8501 (2016)]. This talk will review our progress to date in using these prototype systems to exploit the spatial information obtained from receive beamforming to actively modulate the therapeutic exposures during US-induced BBB opening, following our previously developed single-element internal calibration approach [O’Reilly & Hynynen, Radiology 263, 96-106 (2012)]. We anticipate that this technique will improve the safety and efficacy of MB-mediated BBB opening, as well as other future non-thermal US brain treatments such as cavitation-enhanced ablation, sonothrombolysis, and histotripsy.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".