Effect of Nonlinear Wave Propagation on Temperature Rise in High Frequency Imaging
Bibliographic record
Abstract
The existing safety standards for diagnostic ultrasound devices were developed for low frequency systems (1 to 10 MHz) under the assumption that nonlinear effects are negligible. However, the standards are now being used for high frequency systems (up to 70 MHz) while nonlinear effects are increasingly evident as the working frequency is increased. The goal of this study is to determine if the coverage of nonlinear effects in safety standards in terms of thermal effects is adequate for high frequency systems (>10 MHz). The investigated cases are marginally acceptable according to current safety standards. The safety of each case is then re-assessed after including nonlinear effects in numerical simulations. The thermal results show that strong higher order harmonics can significantly increase the local heat deposition rate. However, at the same time, they lead to an increase in the rate of axial and radial heat conduction, thereby reducing the net impact on the steady-state temperature rise. Also, the effect of higher order harmonics can be significant on the temperature rise at the focal point, while the maximum temperature rise always occurs close to the skin surface. We conclude that current safety standards are adequate in assessing thermal effects in B-mode imaging at high frequencies despite ignoring the extra heat deposited by higher order harmonics.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".