Temperature dependence of harmonics generated by nonlinear ultrasound beam propagation in water
Bibliographic record
Abstract
Ultrasound thermal therapy is used for noninvasive treatment of cancer. For accurate ultrasound based temperature monitoring in thermal therapy, the temperature dependence of acoustic parameters is required. In this study, the temperature dependence of acoustic harmonics was investigated in water. The pressure amplitudes of the transmitted fundamental frequency (p1), and its harmonics (second (p2), third (p3), fourth (p4), and fifth (p5)) generated by nonlinear ultrasound propagation were measured by a calibrated hydrophone in water. The hydrophone was placed at the focal point of a focused 5-MHz transducer (f-number 4.5) to measure the acoustic pressure. Higher harmonics were generated by transmitting a 5-MHz 15-cycle pulse that resulted in a focal positive peak pressure of approximately 0.26 MPa in water. The water temperature was increased from 26 °C to 52 °C in increments of 2 °C. Due to this temperature elevation, the value of p1 decreased by 9%±1.5% (compared to its value at 26 °C) and values of p2, p3, p4, and p5 increased by %5±2%, 22%±8%, 44%±7%, and 55%±5%, respectively. The results indicate that the nonlinear harmonics are highly temperature dependent and their temperature sensitivity increase with the harmonic number. It is concluded that the nonlinear harmonics could potentially be used for ultrasound-based 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 | 0.000 |
| 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".