Real-time magnetic resonance imaging-guided cryoablation of the pulmonary veins with acute freeze-zone and chronic lesion assessment
Bibliographic record
Abstract
AIMS: The goals of this study were to develop a method that combines cryoablation with real-time magnetic resonance imaging (MRI) guidance for pulmonary vein isolation (PVI) and to further quantify the lesion formation by imaging both acute and chronic cryolesions. METHODS AND RESULTS: Investigational MRI-compatible cryoablation devices were created by modifying cryoballoons and cryocatheters. These devices were used in canines (n = 8) and a complete series of lesions (PVI: n = 5, superior vena cava: n = 4, focal: n = 13) were made under real-time MRI guidance. Late gadolinium enhancement (LGE) magnetic resonance imaging was acquired at acute and chronic time points. Late gadolinium enhancement magnetic resonance imagings show a significant amount of acute tissue injury immediately following cryoablation which subsides over time. In the pulmonary veins, scar covered 100% of the perimeter of the ostium of the veins acutely, which subsided to 95.6 ± 4.3% after 3 months. Focal point lesions showed significantly larger acute enhancement volumes compared to the volumes estimated from gross pathology measurements (0.4392 ± 0.28 cm3 vs. 0.1657 ± 0.08 cm3, P = 0.0043). Additionally, our results with focal point ablations indicate that freeze-zone formation reached a maximum area after 120 s. CONCLUSION: This study reports on the development of an MRI-based cryoablation system and shows that with acute cryolesions there is a large area of reversible injury. Real-time MRI provides the ability to visualize the freeze-zone formation during the freeze cycle and for focal lesions reaches a maximum after 120 s suggesting that for maximizing lesion size 120 s might be the lower limit for dosing duration.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".