Non-invasive carotid artery elastography using multi-element synthetic aperture and plane wave imaging: Phantom and <i>in vivo</i> evaluation
Bibliographic record
Abstract
Vascular elastography can visualize the strain distribution in the carotid artery, which governs plaque rupture. In this study, we hypothesize that multi-element synthetic aperture (MSA) imaging, which produces divergent transmit beams can overcome the grating lobes issues associated with compounded plane wave (CPW) imaging, and produce more reliable strain elastograms. To corroborate this hypothesis, we conducted phantom and in vivo studies using both the techniques, and determined the most optimal imaging configuration for carotid elastography. The phantom studies were conducted using cryogel vessel phantoms. We validated the phantom study results in vivo, on healthy volunteers. These studies were performed using a commercially ultrasound scanner (Sonix RP, Ultrasonix Medical Corp., Richmond, BC, Canada), operating at a transmit frequency of 5 MHz. The phantom results demonstrated that the plaque was visible in elastograms from both techniques; however, MSA elastograms had fewer artifacts, with a 12 dB improvement in elastographic contrast to noise ratio relative to CPW imaging. Further, the results from the in vivo study agreed with the phantom results. These results suggest that MSA imaging can produce useful strain elastograms. Our future work will involve further development and more in vivo validation.
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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 | 0.002 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".