Radial modulation contrast imaging using a 20-mhz single-element intravascular ultrasound catheter
Bibliographic record
Abstract
Contrast-enhanced intravascular ultrasound imaging is a promising tool for the characterization of coronary vasa vasorum proliferation, which has been identified as a marker of, and possible etiologic factor in, the development of high-risk atherosclerotic plaques. Resonance-based nonlinear detection methods have required the development of prototype catheters which are not commercially available, thus limiting clinical translation. In this study, we investigated the performances of a radial modulation imaging approach (25/3 MHz combination) using simulations, implemented it on a clinical 20-MHz rotating catheter, and tested it in a wall-less tissue-mimicking flow phantom perfused with lipid-encapsulated microbubbles (MBs). The effects of the phase lag, low-frequency pressure, and MB concentration on the envelope subtracted radial modulation signals were investigated as a function of depth. Our dual-pulse dual-frequency approach produced contrast- specific images with contrast-to-tissue improvements over B-mode of 15.1 ± 2.1 dB at 2 mm and 6.8 ± 0.1 dB at 4 mm depths. Using this imaging strategy, 200-μm-diameter cellulose tubing perfused with MBs could be resolved while surrounding tissue scattering was suppressed. These results raise promise for the detection of coronary vasa vasorum and may ultimately facilitate the detection of plaque at risk for rupture.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".