Cardiac ultrasound multiview fusion using a multicamera tracking system
Bibliographic record
Abstract
This study presents a novel approach to fuse multiple three-dimensional ultrasound scans using a multi-camera tracking system. Recent advances in echocardiography allow real-time three-dimensional dynamic acquisition of the heart. However, one of the major limitations of the three-dimensional echocardiography is the limited field-of-view (FOV), which may lead to an acquisition insufficient to cover the whole geometry of the heart. Recently, methods to improve the FOV and image quality have been introduced by acquiring multiple conventional single-view images with small transducer movements. These methods rely on image registration to align singleview images, and therefore, require sufficient overlap between images to obtain accurate alignment. In this study, we propose a method that relies on a multi-camera tracking system external to the images for image alignment, and therefore, it does not have the constraints of image overlap or quality. The multicamera tracking system is capable of tracking position and orientation information of several objects simultaneously. The accuracy of the alignment of the multi-camera tracking system is superior to the image resolution of echocardiography images. In this pilot project, we used a dynamic heart phantom and a cuboid gelatin phantom to evaluate our method and showed that the proposed method yielded an accurate alignment of image volumes in three-dimensional space.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".