Shear Wave Induced Resonance Elastography of Venous Thrombi: A Proof-of-Concept
Bibliographic record
Abstract
Shear wave induced resonance elastography (SWIRE) is proposed for deep venous thrombosis (DVT) elasticity assessment. This new imaging technique takes advantage of properly polarized shear waves to induce resonance of a confined mechanical heterogeneity. Realistic phantoms (n = 9) of DVT total and partial clot occlusions with elasticities from 406 to 3561 Pa were built for in vitro experiments. An ex vivo study was also performed to evaluate the elasticity of two fresh porcine venous thrombi in a pig model. Transient shear waves at 45-205 Hz were generated by the vibration of a rigid plate (plane wavefront) or by a needle to simulate a radiation pressure on a line segment (cylindrical wavefront). Induced propagation of shear waves was imaged with an ultrafast ultrasound scanner and a finite element method was developed to simulate tested experimental conditions. An inverse problem was then formulated considering the first resonance frequency of the DVT inclusion. Elasticity agreements between SWIRE and a reference spectroscopy instrument (RheoSpectris) were found in vitro for total clots either in plane (r(2) = 0.989) or cylindrical (r(2) = 0.986) wavefront configurations. For total and partial clots, elasticity estimation errors were 9.0 ±4.6% and 9.3 ±11.3%, respectively. Ex vivo, the blood clot elasticity was 498 ±58 Pa within the inferior vena cava and 436 ±45 Pa in the right common iliac vein (p = 0.22). To conclude, the SWIRE technique seems feasible to quantitatively assess blood clot elasticity in the context of DVT ultrasound imaging.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".