Cavitation-enhanced ultrasound heating in vivo: Mechanisms and implications in MR-guided high-intensity focused ultrasound therapy
Bibliographic record
Abstract
Focused ultrasound is currently being developed as a noninvasive thermal ablation technique for benign and cancerous tumors in several organ systems. Although these therapies are designed to ablate tissue purely by thermal means, cavitation can occur. These bubbles can be unpredictable in their timing and location, and often interfere with thermal therapies. Therefore, focused ultrasound techniques have tried to avoid bubbles and their effects. However, gas bubbles in vivo have some potential useful features for therapy. In this research, we design and test in vivo ultrasound exposures that induce cavitation at appropriate times and take advantage of their absorption-enhancing properties while yielding reliable lesion sizes and shapes. In addition, MRI and acoustic methods to monitor and potentially control cavitation induction and the associated therapy are investigated. Finally, histology of the resulting cavitation-enhanced heating lesions is performed to assess the type of tissue damage. We demonstrate that cavitation-enhanced heating can be reliable and useful with the appropriate therapy protocol and application. If induced and monitored properly, cavitation in focused ultrasound therapy could potentially be very beneficial. Early MR-guided HIFU clinical systems that can utilize and monitor cavitation approaches will be presented.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".