Robot-Assisted Multiview Fusion of Three-Dimensional Echocardiography: A Phantom Study
Bibliographic record
Abstract
Echocardiography is one of the most widely used imaging options for diagnosing cardiac diseases. It is inexpensive, portable, and free of ionizing radiation. Despite these advantages, echocardiography has a few limitations, including a limited field of view. One option to overcome these limitations is to scan the heart from different locations and fuse them. However, this approach requires image registration or tracking of the ultrasound transducer’s positions during scanning. Image registration algorithms typically rely on the image features available in images, and their accuracy heavily relies on image quality. Alternatively, previous external tracker-based methods attempted to solve the problem using optical and electromagnetic trackers. In this study, we propose a robot arm to follow the ultrasound transducer and perform a fusion of multiple scans. The proposed approach does not suffer from the requirement of the line of sight between the markers and cameras as in the case of the previous optical tracking-based methods. In this pilot study using a heart phantom, we show that the fusion of multiple echocardiography scans could be achieved using a robotic arm. In addition to solving the field-of-view limitation, the robotic arm-based systems could also lead to a reduction in sonographer strain when operated remotely or autonomously.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".