Dynamic elastography using delay compensated and angularly compounded high frame rate 2D motion vectors
Bibliographic record
Abstract
This paper describes a new ultrasound-based system for high frame rate measurement of periodic motion in 2D for tissue elasticity imaging. The system acquires the RF signals from the region of interest from multiple steering angles in order to reconstruct the 2D motion from ID estimation along each angle. To increase the temporal resolution, the acquisition area is divided into groups of scan lines called sectors. Each sector is acquired multiple times before moving onto the next sector. Following the data acquisition, ID motions are estimated along the beam direction from the sequences of echo signals. Using a recently introduced delay compensation algorithm, the intra- and inter-sector delays in the motion estimates are compensated to create high frame rate images. In-plane 2D motion vectors are then reconstructed from these delay compensated ID motions. Finally, modulus images are estimated from these 2D motion vectors using planar algebraic inversion of the Helmholtz equation. The performance of the system is validated quantitatively using a commercial elasticity phantom. At frame rate of 1250 Hz, phantom Young's moduli of 29kPa, 6kPa, and 54kPa for the background, the soft inclusion, and the hard inclusion of a phantom, are estimated to be 30kPa, 11kPa, and 53kPa, respectively, for an excitation frequency of 150 Hz.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".