P381Retrospective registration can provide good concordance between perfusion data from 3D nuclear imaging and electrophysiological data from EnSite Velocity mapping system
Bibliographic record
Abstract
Introduction: Catheter ablation of scar-related ventricular tachycardia (VT) remain challenging partly due to the difficulty of accurate identification of scar tissue. Improvement of arrhythmogenic substrate characterization could be achieved through myocardial perfusion imaging (SPECT/CT) and voltage mapping (VM) integration. Purpose: This study describes the concordance between SPECT/CT and VM characterization of ventricular scar and heathy myocardium post procedure and assesses the role of different landmarks in the retrospective 3D surface registration process. Methods: Ischemic VT subjects underwent SPECT/CT imaging prior to left ventricular electroanatomical mapping (EAM) with EnSite Velocity cardiac mapping system. Post-procedure, the SPECT/CT, VM data and ablation lesions went through a retrospective co-registeration in the EnSite system (Fig A) and exported for supplemantary analysis. Perfusion data were scored from 0 (> 70% perfusion, healthy myocardium) to 4 (≤ 40%, myocardial scar). The voltage data were scored between 0 (> 2.5 mV, healthy myocardium) and 4 (< 0.5 mV, myocardial scar). Then both perfusion and voltage scores were grouped into each of the 17 segments and two scar percentages were computed for each segment, one using the perfusion scores and the second using the voltage scores. Percentages above a threshold of 50 were assumed to be scar segments. Segments corresponding to endocardial regions of the left ventricle where no voltage recordings were made with the catheter were discarded. Concordance (scar and healthy segments correctly matched overall segments), sensitivity (proportion of scar segments correctly matched) and specificity (proportion of healthy segments correctly matched) between the two modalities was evaluated (Fig B). Results: Seven subjects (100% male, 67 ± 7 years old, LVEF 26 ± 10%) underwent voltage mapping and SPECT/CT integration. Through retrospective registration at least 60% of the segments classified as scar by the perfusion data was correctly matched with the voltage mapping results. In 3/7 patients voltage mapping lead to a 30% overestimation of the scar area. A concordance of 70.0% was found among all subjects between VM and SPECT/CT for identification of CSeg as scar or healthy with VM showing a 81.6% sensitivity and 60.9% specificity when using SPECT/CT as a gold standard. 94% of the applied ablation lesions were located in CSeg identified as scar by SPECT/CT. Conclusion: Following retrospective registration concordance between SPECT/CT and voltage mapping data is possible for VT scar identification. The registration process as well as the asymetry in the endocardial mapping resolution (i.e. high around the VT scar and low in healthy cardiac tissue) may explain why sensitivity is better than specificity. Value of VM-SPECT/CT integration will be evaluated prospectively in a new series of ischemic VT ablation procedures. Abstract P381 Figure.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".