Postinfarction Ventricular Tachycardia Substrate Characterization: A Comparison Between Late Enhancement Magnetic Resonance Imaging and Voltage Mapping Using an MR-Guided Electrophysiology System
Bibliographic record
Abstract
Catheter ablation of ventricular tachycardia (VT) is preceded by characterization of the myocardial substrate via electroanatomical voltage mapping (EAVM). The purpose of this study was to characterize the relationship between chronic myocardial fibrotic scar detected by multicontrast late enhancement (MCLE) MRI and by EAVM obtained using an MR-guided electrophysiology system, with a final aim to better understand how these measures may improve identification of potentially arrhythmogenic substrates. Real-time MR-guided EAVM was performed in six chronically infarcted animals in a 1.5T MR system. The MCLE images were analyzed to identify the location and extent of the fibrotic infarct. Voltage maps of the left ventricle (LV) were created with an average of 231 ± 35 points per LV. Correlation analysis was conducted between bipolar voltage and three MR parameters (infarct transmurality, tissue categorization into healthy and scar classes, and normalized relaxation rate R1). In general, tissue regions classified as scar by normalized R1 values were well correlated with locations with low bipolar voltage values. Moreover, our results demonstrate that MRI information (transmurality, tissue classification, and relaxation rate) can accurately predict areas of myocardial fibrosis identified with bipolar voltage mapping, as demonstrated by ROC analysis. MCLE can help overcome limitations of bipolar voltage mapping including long durations and lower spatial discrimination and may help identify the sites within scars, which are commonly believed to trigger arrhythmic events in postinfarction patients.
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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".