Real‐Time MRI‐Guided Cardiac Cryo‐Ablation: A Feasibility Study
Bibliographic record
Abstract
INTRODUCTION: MRI-based ablation provides an attractive capability of seeing ablation-related tissue changes in real time. Here we describe a real-time MRI-based cardiac cryo-ablation system. METHODS: Studies were performed in canine model (n = 4) using MR-compatible cryo-ablation devices built for animal use: focal cryo-catheter with 8 mm tip and 28 mm diameter cryo-balloon. The main steps of MRI-guided cardiac cryo-ablation procedure (real-time navigation, confirmation of tip-tissue contact, confirmation of vessel occlusion, real-time monitoring of a freeze zone formation, and intra-procedural assessment of lesions) were validated in a 3 Tesla clinical MRI scanner. RESULTS: The MRI compatible cryo-devices were advanced to the right atrium (RA) and right ventricle (RV) and their position was confirmed by real-time MRI. Specifically, contact between catheter tip and myocardium and occlusion of superior vena cava (SVC) by the balloon was visually validated. Focal cryo-lesions were created in the RV septum. Circumferential ablation of SVC-RA junction with no gaps was achieved using the cryo-balloon. Real-time visualization of freeze zone formation was achieved in all studies when lesions were successfully created. The ablations and presence of collateral damage were confirmed by T1-weighted and late gadolinium enhancement MRI and gross pathological examination. CONCLUSION: This study confirms the feasibility of a MRI-based cryo-ablation system in performing cardiac ablation procedures. The system allows real-time catheter navigation, confirmation of catheter tip-tissue contact, validation of vessel occlusion by cryo-balloon, real-time monitoring of a freeze zone formation, and intra-procedural assessment of ablations including collateral damage.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".