Image-guided ultrasound phased arrays are a disruptive technology for non-invasive therapy
Bibliographic record
Abstract
Focused ultrasound offers a non-invasive way of depositing acoustic energy deep into the body, which can be harnessed for a broad spectrum of therapeutic purposes, including tissue ablation, the targeting of therapeutic agents, and stem cell delivery. Phased array transducers enable electronic control over the beam geometry and direction, and can be tailored to provide optimal energy deposition patterns for a given therapeutic application. Their use in combination with modern medical imaging for therapy guidance allows precise targeting, online monitoring, and post-treatment evaluation of the ultrasound-mediated bioeffects. In the past there have been some technical obstacles hindering the construction of large aperture, high-power, densely-populated phased arrays and, as a result, they have not been fully exploited for therapy delivery to date. However, recent research has made the construction of such arrays feasible, and it is expected that their continued development will both greatly improve the safety and efficacy of existing ultrasound therapies as well as enable treatments that are not currently possible with existing technology. This review will summarize the basic principles, current statures, and future potential of image-guided ultrasound phased arrays for therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.002 |
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".