Noninvasive tissue temperature estimation using nonlinear ultrasound harmonics
Bibliographic record
Abstract
Non-invasive tissue temperature estimation is important in thermal therapies for having an efficient treatment. A noninvasive ultrasonic technique for monitoring tissue temperature changes is proposed based on the changes in the harmonics of ultrasound backscatter as a function of temperature. The backscattered pressure amplitudes of the fundamental frequency (p1), the second (p2) and the third (p3) harmonics generated by nonlinear ultrasound propagation and the ratios of the second and the third harmonics over the fundamental frequency (p2/p1 and p3/p1) were investigated as a function of temperature. The acoustic harmonics were generated and detected with a commercial high frequency ultrasound imaging system in pulse-echo mode. The experiments were performed on tissue-mimicking gel phantoms and ex vivo bovine muscle tissues. The temperature was increased from 26°C to 46°C in increments of 2°C. The average values of p1, p2, p3, p2/p1, p3/p1 increased by 14%, 50%, 117%, 37% and 92% for the gel phantoms, and for the tissue samples increased by 29%, 50%, 170%, 10% and 109%, respectively. The results indicate that the harmonic amplitudes and their ratios are highly sensitive to propagation medium’s temperature and could potentially be used for noninvasive ultrasound thermometry.
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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 | 0.001 |
| 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".