<title>Image-based retrospective cardiac gating for three-dimensional intravascular ultrasound imaging</title>
Bibliographic record
Abstract
Three-dimensional (3D) intravascular ultrasound provides valuable insight into the tissue characteristics of the coronary wall and plaque composition. However, artifacts due to cardiac motion and vessel wall pulsation limit the accuracy and variability of coronary lumen and plaque volume measurement in 3D IVUS images. ECG-gated image acquisition can overcome these artifacts but results in lengthy acquisition times. Our goal is to reconstruct a 3D IVUS image with negligible vessel pulsation artifacts, by developing an image-based retrospective gating method to track 2D IVUS images belonging to the same cardiac phase. Our approach involves selecting 2D IVUS images belonging to the same cardiac phase from an asynchronously acquired series, by tracking the changing lumen contour over the cardiac cycle. The algorithm was tested using a custom-built coronary phantom and on patient images. 3D non-gated and gated IVUS images were assembled and compared. The extent of pulsation artifacts in the 3D images was estimated by measuring the standard deviation in the shift in the position of the lumen boundary in each cross-sectional slice over the 3D IVUS image. A reduction in pulsation artifact of over 97% was observed in the 3D image assembled using our method.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".