“3D Fusion” Echocardiography Improves 3D Left Ventricular Assessment: Comparison with 2D Contrast Echocardiography
Bibliographic record
Abstract
BACKGROUND: Three-dimensional fusion echocardiography (3DFE) is a novel postprocessing approach that utilizes imaging data acquired from multiple 3D acquisitions. We assessed image quality, endocardial border definition, and cardiac wall motion in patients using 3DFE compared to standard 3D images (3D) and results obtained with contrast echocardiography (2DC). METHODS: Twenty-four patients (mean age 66.9 ± 13 years, 17 males, 7 females) undergoing 2DC had three, noncontrast, 3D apical volumes acquired at rest. Images were fused using an automated image fusion approach. Quality of the 3DFE was compared to both 3D and 2DC based on contrast-to-noise ratio (CNR) and endocardial border definition. We then compared clinical wall-motion score index (WMSI) calculated from 3DFE and 3D to those obtained from 2DC images. RESULTS: Fused 3D volumes had significantly improved CNR (8.92 ± 1.35 vs. 6.59 ± 1.19, P < 0.0005) and segmental image quality (2.42 ± 0.99 vs. 1.93 ± 1.18, P < 0.005) compared to unfused 3D acquisitions. Levels achieved were closer to scores for 2D contrast images (CNR: 9.04 ± 2.21, P = 0.6; segmental image quality: 2.91 ± 0.37, P < 0.005). WMSI calculated from fused 3D volumes did not differ significantly from those obtained from 2D contrast echocardiography (1.06 ± 0.09 vs. 1.07 ± 0.15, P = 0.69), whereas unfused images produced significantly more variable results (1.19 ± 0.30). This was confirmed by a better intraclass correlation coefficient (ICC 0.72; 95% CI 0.32-0.88) relative to comparisons with unfused images (ICC 0.56; 95% CI 0.02-0.81). CONCLUSION: 3DFE significantly improves left ventricular image quality compared to unfused 3D in a patient population and allows noncontrast assessment of wall motion that approaches that achieved with 2D contrast echocardiography.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".