Focused ultrasound surgery monitoring using local harmonic motion imaging
Bibliographic record
Abstract
The present study established the feasibility of a real-time monitoring technique for focused ultrasound (FUS) lesion formation using localized harmonic motion imaging. This oscillatory motion was generated within tissues by periodically induced radiation force using a FUS transducer (oscillatory motion frequencies between 50 and 300 Hz). The harmonic motion was estimated using cross correlation of rf ultrasonic signals acquired at different instances during the motion by using a separate US diagnostic transducer excited by a pulser/receiver. The technique was evaluated on rabbit muscle under in vitro and in vivo conditions. Fourteen FUS lesions were induced in vivo inside an MR scanner to obtain simultaneous ultrasound harmonic motion tracking and MR thermometry. The calculated maximum amplitude of the induced harmonic motion before and after the lesion formation was significantly different for all the tested motion frequencies. During the FUS exposure a drop in the maximum amplitude value was observed and could be associated to a lesion formation. After the lesion formation, the harmonic motion was measured across the exposure region, and the lesion was detected as a reduction in the maximum motion amplitude value at the coagulated region. [Work supported by NIH Grant R21/R33 CA102884-01.]
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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) | 0.001 | 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".