Real-Time Magnetic Resonance Imaging–Guided Stenting of Aortic Coarctation With Commercially Available Catheter Devices in Swine
Bibliographic record
Abstract
BACKGROUND: Real-time MR imaging (rtMRI) is now technically capable of guiding catheter-based cardiovascular interventions. Compared with x-ray, rtMRI offers superior tissue imaging in any orientation without ionizing radiation. Translation to clinical trials has awaited the availability of clinical-grade catheter devices that are both MRI visible and safe. We report a preclinical safety and feasibility study of rtMRI-guided stenting in a porcine model of aortic coarctation using only commercially available catheter devices. METHOD AND RESULTS: Coarctation stenting was performed wholly under rtMRI guidance in 13 swine. rtMRI permitted procedure planning, device tracking, and accurate stent deployment. "Active" guidewires, incorporating MRI antennas, improved device visualization compared with unmodified "passive" nitinol guidewires and shortened procedure time (26+/-11 versus 106+/-42 minutes; P=0.008). Follow-up catheterization and necropsy showed accurate stent deployment, durable gradient reduction, and appropriate neointimal formation. MRI immediately identified aortic rupture when oversized devices were tested. CONCLUSIONS: This experience demonstrates preclinical safety and feasibility of rtMRI-guided aortic coarctation stenting using commercially available catheter devices. Patients may benefit from rtMRI in the future because of combined device and tissue imaging, freedom from ionizing radiation, and the ability to identify serious complications promptly.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".