A Curl-Based Approach to Ultrasound Elastography
Bibliographic record
Abstract
The past two decades have witnessed the development of a new medical imaging modality: tissue elastography. The contrast in the images produced by an elastography system is based on the tissue elasticity, hence these images are called elastograms. Tissue elasticity is of clinical interest, because it is often correlated with pathology [1]. Different approaches to tissue elastography have emerged [2, 3]. In this article we report a tissue elastography system and its implementation on an ultrasound machine which provides consistent elastograms of a commercial quality assurance elastography phantom. The system uses our previously developed high frame rate sequencing and phase compensation techniques to measure axial and lateral motions at a typical frame rate of 1.25 kHz [4]. The system uses the curl of the displacements in a direct inversion algorithm to reconstruct elasticity. The most important benefit of this method is that the obtained elastograms are not dependent on the boundary conditions or the shape, size or position of the exciter, and as a result, the elastograms have fewer artifacts originating from these factors. The curl of the displacement has been used in magnetic resonance elastography (MRE) before, together with the direct inversion of the wave equation [5] and promising results have been obtained.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".