3D fusion echocardiography improves 3D left ventricular assessment: comparison with 2D contrast echocardiography
Bibliographic record
Abstract
Purpose: Real time 3D (RT3D) echocardiography has the potential to provide rapid acquisition of whole heart volumes for clinical use where fast, real time assessment of ventricular function is important, such as stress echocardiography. In clinical practice RT3D image quality is limited by echocardiographic windows and stitching artefacts. We hypothesised that fusion of multiple, sequential RT3D volume datasets may improve image quality. We compare this approach to the gold standard reference image for stress echocardiography, 2D contrast echocardiography. Methods: 60 patients had RT3D volumes (1 beat and 4beats) acquired according to 2 protocols. In Protocol A, the 4 chamber view on 2D imaging was visually assessed and a RT3D dataset acquired which was fused with a further two 3D volumes that were acquired following small probe manoeuvres. In protocol B the RT3D volumes (4 beat) were acquired of the optimal 4, 3 and 2 chamber views and these were fused together. Finally 2D contrast images were obtained of 4, 3 and 2 chamber view. The fused, standard RT3D and 2D contrast views were analysed to compare (i) quality (using the standard segmental approach) and (ii) contrast to noise ratio (CNR). Contrast-to-noise ratio (CNR) was used as a quantitative measurement of the image quality- defined as the ratio of the signal intensity differences between image regions (myocardium) and the image noise (blood pool). Each segment was assigned a rating 0-3 depending on the amount of visualised endocardial border defined as: 0 = <50%, 1 = 50-75%, 2 = 75-99%, 3 = 100%. 2 readers analysed images. Bland Altman agreement was used to assess interobserver relationship. Results: 3550 segments in total were analysed. Segmental image quality of both 1 beat and 4 beat standard RT3D views improved significantly with Protocol A fusion (mean 1.4 vs. 1.7 & 1.8 vs. 2.1, P<0.005), although were lower than that seen for 2D contrast echocardiography (mean 2.8). Protocol B fusion improved image quality further (2.1 vs. 2.3, P<0.05) and the CNR was improved when compared to standard RT3D volumes (6.6 vs. 9.0, P<0.05) and was the same as that seen for 2D contrast echocardiography (9.0 vs. 9.0). Interobserver agreement showed a mean difference in image quality assessment between the two readers of 0.2-0.4 image points. Conclusions: 3D fusion significantly improves LV segmental image quality and contrast to noise ratio, approaching that of 2D contrast echocardiography. This may be of clinical relevance in reducing the necessity for contrast echocardiography as well as potential use in 3D stress 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".