P775Extra-cardiac and intra-cardiac landmarks used in combination can increase registration accuracy between nuclear imaging and electro-anatomical 3D geometries
Bibliographic record
Abstract
Introduction: Due to technical limitations, patients with implanted cardiac defibrillator scheduled for ventricular ablation are guided to imaging modalities like myocardial perfusion imaging (SPECT/CT). Geometry integration from the latter, due to lower resolution, affects the registration quality. Purpose: This study seeks to determine whether additional extra-cardiac landmarks can improve registration accuracy between SPECT/CT and electro-anatomical mapping (EAM) geometry. Methods: Ischemic VT subjects underwent SPECT/CT imaging prior to left ventricular EAM. Group 1 (N=6) used intra-cardiac (IC) landmarks only (i.e. right and left cardiac ventricular chambers, implanted device leads, the coronary sinus (CS), ascending aorta) for SPECT/CT and EAM geometry registration. In group 2 (N=5), the SPECT/CT geometries included vertebras and corresponding ribs (the extra-cardiac landmarks) in addition to the IC. Extra-cardiac fiducials were established in the descending aorta at one of the T9-T12 and T3-T5 vertebras identified on the fluoroscopy (see figure below). Within the left ventricle, the mitral valve and apex were targeted to establish fiducials in both groups. In each subject, registration was evaluated by first averaging the data by segment and then fitting a linear regression between voltage and perfusion data projected on the SPECT/CT geometry. Results: Eleven subjects (100% male, 65 ± 9 years old, LVEF 31 ± 10%) underwent EAM and SPECT/CT integration. Registrations in Group 1 allowed the projection of voltage data over 82.4% of the LV geometry from SPECT/CT compared to 98.8% for Group 2 (p = 0.03). A significant linear regression model (p < 0.05) between perfusion and voltage was established for 50% of the geometry registered in Group 1 compared to 100% of the geometry registered in Group 2. Despite the added fiducial points in Group 2, procedure and fluoroscopy time were not significantly different. Conclusion: In combination with IC, extra-cardiac landmarks, identified on both the fluoroscopy and SPECT/CT geometry, can significantly improve the accuracy of the registration without significant impact on the procedure or fluoroscopy time. This suggests that the extra time to collect the fiducial information was later compensated by higher accuracy during the mapping/ablation procedure. Abstract P775 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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".