Transcranial bubble activity mapping for therapy and imaging
Bibliographic record
Abstract
Bubble-mediated ultrasound therapies in the brain, such as targeted disruption of the blood-brain barrier (BBB) or cavitation-enhanced stroke treatments, are being increasingly investigated due to their potential to revolutionize the treatment of brain disorders. Due to the fact that they are non-thermal in nature, these therapies must be monitored by acoustic means to ensure efficacy and safety. A sparse, 128-element hemispherical receiver array (612 kHz) was integrated within a 306 kHz therapy array. The receiver arrangement was optimized through numerical simulations. The array was characterized on the benchtop to map the activity of bubbles in a tube phantom through an ex vivo human skullcap. In vivo the array was used to map bubble activity in small animal models during microbubble-mediated BBB disruption. The array was investigated as well for diagnostic purposes, imaging transcranial structures filled with very dilute concentrations of microbubbles. A spiral tube phantom with tube diameter of 255 µm was imaged, using a non-invasive phase correction technique, through an ex vivo human skullcap by mapping the activity from single bubbles. Applying super-resolution techniques, an image of the spiral phantom was produced that was comparable to an image obtained in a small-specimen micro CT.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".