Influence of guided waves in bone on pulse‐inversion contrast‐enhanced ultrasound
Bibliographic record
Abstract
PURPOSE: Guided waves generated from bone cortex inevitably act on microbubbles flowing through skeletal muscle capillaries in contrast-enhanced ultrasound (CEUS) and might influence the image quality. However, the action mechanism underlying the guided waves influence is still unknown, especially under contrast pulse-inversion transmission mode. This study aimed to clarify the influence of guided waves on pulse-inversion CEUS, which was investigated via in vitro infusion experiments. METHOD: Tibia guided waves were detected at pulse-inversion transmission and then characterized by using a short-time Fourier transform energy distribution. Using results at normal incidence as a baseline, the influence of guided wave dispersion on the contrast and resolution of pulse-inversion CEUS was investigated at an oblique incidence through continuous microbubbles infusion experiments in a vessel-tibia flow phantom. RESULTS: Frequency-dispersive property of tibia guided waves was observed at phases 0° and 180°, which improved the contrast of CEUS and reduced its resolution. Pulse-inversion CEUS balanced the contrast enhancement and resolution degeneration induced by guided waves. By contrast, contrast-to-tissue ratio of pulse-inversion CEUS increased by up to 109.1 ± 13.2% (P < 0.05) due to guided waves and its resolution was up to 0.9 ± 0.1 times that of baseline. CONCLUSIONS: Alterations of contrast and resolution in pulse-inversion CEUS induced by guided waves might provide an additional assessment for the capillary perfusion in the skeletal muscle near the bone cortex.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".