The effect of frequency adaptation according to the attenuation coefficient and focus depth on radiation force amplitude and estimated displacements
Bibliographic record
Abstract
In remote dynamic elastography, amplitudes of generated displacement fields are directly related to the amplitude of the radiation force. Therefore, displacement improvement for better tissue characterization requires the optimization of the radiation force by increasing the push duration and/or the excitation amplitude of the transducer. The main problem of this approach is that the Food and Drug Administration (FDA) thresholds for medical applications, and transducer limitations may be easily exceeded. In the present study, the effect of the frequency used for the generation of radiation force on the amplitude of the displacement field is investigated. The aim is to apply the adaptive radiation force to increase the displacement amplitude. We found that amplitudes of displacements generated by adapted radiation force sequences are greater than those generated by non-adapted ones. The obtained gains were between 20% and 158% depending on the focus depths and the attenuation of the tested phantom. The signal to noise ratio was also improved by more than four times. We conclude that frequency adaptation is a complementary technique that may be used for the optimization of displacement amplitude. This technique can be used safely to optimize the deposited local acoustic energy, without increasing the risk of damaging tissues and transducers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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".