Design of a phased-array for radiation force generation following a closed path
Bibliographic record
Abstract
This work demonstrates with numerical simulations, the feasibility of an ultrasound probe for the generation of radiation forces in set of points following a path surrounding a tumor. Such strategy is adapted to induce resonance elastography of breast tumors and/or to increase displacement magnitudes induced by low frequency shear waves. Transducer elements were based on 1-3 piezocomposite material. 3D simulations combining the finite element method and boundary element method with periodic boundary conditions in the elevation direction were used to predict acoustic wave radiation in the breast. The crosstalk between neighbor elements was not taken into account. The coupling factor of the piezocomposite material and the radiated power of the transducer were optimized. The transducer electrical impedance was targeted to 50 Ω. The final probe was simulated by assembling the designed transducer to build an octagonal phased-array, with 256 elements on each edge. Using dynamic transmitter beamforming techniques, the electrical excitation that generates the radiation force along a path and resulting acoustic pattern in the breast were evaluated. Transducers central frequency was 4.5 MHz; they were able to deliver enough power and could generate the radiation force with a relatively low level of voltage excitation. Magnitude and orientation of the acoustic intensity (radiation force) at any point of a path were controlled.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".