Multi-View 3-D Fusion Echocardiography: Enhancing Clinical Feasibility with a Novel Processing Technique
Bibliographic record
Abstract
A novel system for fusing 3-D echocardiography data sets from complementary acoustic windows was evaluated in 12 healthy volunteers and 12 patients with heart failure. We hypothesized that 3-D fusion would enable 3-D echocardiography in patients with limited acoustic windows. At least nine 3-D data sets were recorded, while three infrared cameras tracked the position and orientation of the transducer and chest respiratory movements. Corresponding 2-D planes of the fused 3-D data sets and of single-view 3-D data sets were assessed for image quality and compared with measurements of left ventricular function obtained with contrast 2-D echocardiography. The signal-to-noise ratio in accurately fused 3-D echocardiography recordings improved by 55% in systole (p < 0.001) and 47% in diastole (p < 0.00001) compared with the apical single-view recordings. The 3-D data sets acquired during short breath holds were successfully fused in 11 of 12 patients. The improvement in endocardial border definition (from 11.7 ± 6.0 to 24.0 ± 3.3, p < 0.01) enabled quantitative assessment of left ventricular function in 10 patients, with no significant difference in ejection fraction compared with contrast 2-D echocardiography. In patients with heart failure and limited acoustic windows, the novel fusion protocol provides 3-D data sets suitable for quantitative analysis of left ventricular function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".