Bandpass sampling of high-frequency tissue motion
Bibliographic record
Abstract
The characterization of tissue viscoelastic properties requires the measurement of tissue motion over a region of interest at frequencies that significantly exceed the frame rates of conventional ultrasound systems. In this paper, we propose that the bandpass sampling technique be applied to tissue motion sampling. With this approach, high-frequency signals limited to a frequency band can be sampled and reconstructed without aliasing at a sampling frequency that is lower than the Nyquist rate. We first review this approach and discuss the selection of the tissue excitation frequency band and of the feasible sampling frequencies that allow signal reconstruction without aliasing. We then demonstrate the approach using simulations based on the finite element method and ultrasound simulations. Finally, we perform experiments on tissue-mimicking materials and demonstrate accurate motion estimation using a lower sampling rate than that required by the conventional sampling theorem. The estimated displacements were used to measure the elasticity and viscosity in a phantom in which an inclusion has been correctly delineated. Thus, with bandpass sampling, it is feasible to use conventional beamforming on diagnostic ultrasound systems to perform high-frequency dynamic elastography. The method is simple to implement because it does not require beam interleaving, additional hardware, or synchronization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".