Theoretical considerations for ultrasound contrast agent amplitude modulation techniques at high frequencies.
Bibliographic record
Abstract
Increasing the nonlinear response of ultrasound contrast agents (UCAs) in response to two consecutive pulses in amplitude modulation (AM) techniques can significantly increase the contrast to tissue ratio (CTR). It has been recently shown that one way to increase the nonlinearity is to take advantage of the buckling behavior of lipid shell bubbles. However, this method is limited to frequencies below the resonance of the bubble. To achieve enhanced nonlinearity at high frequencies, the oscillations of the UCAs should be optimized. In this work, the Hoff model was solved for a wide range of the parameters of the UCAs. Results showed that when the bubbles are sonicated with a frequency slightly less than the integer multiples of its resonance frequency (e.g., 2.7 and 3.6), and above a pressure threshold (PT), there is a significant increase in the UCA stable radial oscillations, which corresponds to a significant backscattered signal enhancement. If the sonication frequency of the AM technique is optimized according to the mentioned frequencies, and the amplitudes of the AM first and second pulses are smaller and greater than this PT, the CTR of the AM technique may be enhanced significantly.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".