Magnetic Navigation for Thoracic Aortic Stent-graft Deployment Using Ultrasound Image Guidance
Bibliographic record
Abstract
We propose a system for thoracic aortic stent-graft deployment that employs a magnetic tracking system (MTS) and intraoperative ultrasound (US). A preoperative plan is first performed using a general public utilities-accelerated cardiac modeling method to determine the target position of the stent-graft. During the surgery, an MTS is employed to track sensors embedded in the catheter, cannula, and the US probe, while a fiducial landmark based registration is used to map the patient's coordinate to the image coordinate. The surgical target is tracked in real time via a calibrated intraoperative US image. Under the guidance of the MTS integrated with the real-time US images, the stent-graft can be deployed to the target position without the use of ionizing radiation. This navigation approach was validated using both phantom and animal studies. In the phantom study, we demonstrate a US calibration accuracy of 1.5 ± 0.47 mm, and a deployment error of 1.4 ± 0.16 mm. In the animal study, we performed experiments on five porcine subjects and recorded fiducial, target, and deployment errors of 2.5 ± 0.32, 4.2 ± 0.78, and 2.43 ± 0.69 mm, respectively. These results demonstrate that delivery and deployment of thoracic stent-graft under MTS-guided navigation using US imaging is feasible and appropriate for clinical application.
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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 | 0.000 |
| 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".