Real-time magnetic resonance-guided radiofrequency ablation and lesion evaluation in an magnetic resonance-compatible isolated beating pig heart platform
Bibliographic record
Abstract
Background: Interventional cardiovascular magnetic resonance imaging (MRI) offers real-time, radiation-free guidance for complex procedures such as myocardial ablation, marking a promising advance in electrophysiology. However, further development is limited by challenges in magnetic resonance (MR)-compatible instrument testing, MRI sequence validation, and accurate correlation with histopathology, hindered by the limitations of in vivo tissue evaluation. Objective: This study investigated the feasibility of real-time MR-guided radiofrequency (RF) ablation in an MR-compatible isolated beating pig heart platform and characterized ablation lesions using MRI and histopathology. Methods: A heart from a pig slaughtered for human consumption was prepared under regulatory guidelines and connected to a custom-built, MR-compatible perfusion platform supporting left ventricular function in both Langendorff and working modes. Autologous heparinized blood circulated at physiological pressures and temperatures. MR-guided catheter navigation and RF ablation were performed on a Philips 3T scanner using active catheter tracking. Native T1 and T2 mapping were acquired before and after ablation. Lesions were confirmed by histologic analysis. Results: RF ablation (50 W, 60 seconds) was successfully performed at 5 left ventricular sites. MRI showed focal reductions in T1 (936 ± 80 ms) surrounded by elevated T1 (1357 ± 18 ms) and T2 values (86 ± 10 ms) compared with nonablated myocardium (T1 1192 ± 26 ms; T2 66 ± 6 ms), consistent with necrosis and edema. Histology confirmed a necrotic core with a surrounding rim showing contraction band necrosis and erythrocyte extravasation. Conclusion: This study demonstrates the feasibility of real-time MR-guided ablation in a beating pig heart platform. The setup allows high-resolution lesion assessment and histologic correlation, supporting future developments in MR-guided therapies.
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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 | 0.000 | 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) | 0.001 | 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".