Design of a phased array for the generation of adaptive radiation force along a path surrounding a breast lesion for dynamic ultrasound elastography imaging
Bibliographic record
Abstract
This work demonstrates, with numerical simulations, the potential of an octagonal probe for the generation of radiation forces in a set of points following a path surrounding a breast lesion in the context of dynamic ultrasound elastography imaging. Because of the in-going wave adaptive focusing strategy, the proposed method is adapted to induce shear wave fronts to interact optimally with complex lesions. Transducer elements were based on 1-3 piezocomposite material. Three-dimensional 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 a targeted region of interest. The coupling factor of the piezocomposite material and the radiated power of the transducer were optimized. The transducer's electrical impedance was targeted to 50 Ω. The probe was simulated by assembling the designed transducer elements to build an octagonal phased-array with 256 elements on each edge (for a total of 2048 elements). The central frequency is 4.54 MHz; simulated transducer elements are able to deliver enough power and can generate the radiation force with a relatively low level of voltage excitation. Using dynamic transmitter beamforming techniques, the radiation force along a path and resulting acoustic pattern in the breast were simulated assuming a linear isotropic medium. Magnitude and orientation of the acoustic intensity (radiation force) at any point of a generation path could be controlled for the case of an example representing a heterogeneous medium with an embedded soft mechanical inclusion.
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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 | 0.000 |
| 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) | 0.001 | 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".