Intrinsic Contrast for Characterization of Acute Radiofrequency Ablation Lesions
Bibliographic record
Abstract
BACKGROUND: Both intrinsic contrast (T₁ and T₂ relaxation and the equilibrium magnetization) and contrast agent (gadolinium)-enhanced MRI are used to visualize and evaluate acute radiofrequency ablation lesions. However, current methods are imprecise in delineating lesion extent shortly after the ablation. METHODS AND RESULTS: Fifteen lesions were created in the endocardium of 13 pigs. A multicontrast inversion recovery steady state free precession imaging method was used to delineate the acute ablation lesions, exploiting T₁-weighted contrast. T₂ and Mo(*) maps were also created from fast spin echo data in a subset of pigs (n=5) to help characterize the change in intrinsic contrast in the lesions. Gross pathology was used as reference for the lesion size comparison, and the lesion structures were confirmed with histological data. In addition, a colorimetric iron assay was used to measure ferric and ferrous iron content in the lesions and the healthy myocardium in a subset of pigs (n=2). The lesion sizes measured in inversion recovery steady state free precession images were highly correlated with the extent of lesion core identified in gross pathology. Magnetic resonance relaxometry showed that the radiofrequency ablation procedure changes the intrinsic T₁ value in the lesion core and the intrinsic T₂ in the edematous region. Furthermore, the T₁ shortening appeared to be correlated with the presence of ferric iron, which may have been associated with metmyoglobin and methemoglobin in the lesions. CONCLUSIONS: The study suggests that T₁ contrast may be able to separate necrotic cores from the surrounding edematous rims in acute radiofrequency ablation lesions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".