Acceleration of ultrasound thermal therapy by patterned acoustic droplet vaporization
Bibliographic record
Abstract
One application of acoustic droplet vaporization (ADV), a method of converting biocompatible microdroplets into microbubbles, is to enhance locally high intensity focused ultrasound (HIFU) therapy. Two objectives are pursued here: (1) the controlled creation of a bubble trench prior to HIFU using ADV and (2) use of the trench for increasing ablation volumes, lowering acoustic powers, and decreasing therapy duration. Thermally responsive phantoms were made with perfluorocarbon emulsion. Compound lesions were formed in a laboratory setting and a clinical magnetic resonance imaging (MRI)-guided HIFU system. Linear and spiral patterned compound lesions were generated in trenches. A larger fraction of the HIFU beam is contained to increase the generation of heat. Using the laboratory system, a 90 mm linear length spiral trench was formed in 30 s with mechanical beam steering. Comparatively, the clinical HIFU system formed a 19.9 mm linear length spiral trench in approximately 1 s with electronic beam steering. Lesions were imaged optically and with MRI. A uniform thermal ablation volume of 3.25 mL was achieved in 55.4 s (4-times faster than standard clinical HIFU and 14-times larger volume versus sum of individual lesions). Single lesions showed a 400% volume increase.
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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".