Abstract 1089: Noninvasive Assessment Of Aortic Biomechanics Using 4-D Flow And Cardiovascular Magnetic Resonance In A Porcine Model Of Aortic Coarctation
Bibliographic record
Abstract
Objectives: Aortic coarctation (COA) is a congenital heart disease that results from narrowing of the aorta, contributing to increased cardiovascular morbidity. Current animal models are limited by stenotic mechanism and study longevity. We introduce a porcine model of COA, treated with a novel growth stent and imaged with advanced MRI techniques, to obtain hemodynamic metrics. Methods: A total of 14 piglets, 4 sham, 4 coarcted controls (CC) and 6 coarcted stents (CS), underwent surgical creation of a COA. The timeline is detailed in Figure 1. Serial imaging was obtained on a 3.0T MRI scanner. Hemodynamic metrics were acquired in predefined vascular planes (Ensight, CEI) to assess net flow, velocities and helical or vortical flow. CMR image analysis using cvi42 (Circle, Canada) included T1 maps and cardiac function. Results: Analyses are shown at 20 weeks. For CMR, there were no differences in cardiac function metrics, left ventricular mass or T1 mapping. In the 4D Flow analysis, shown in Figure 2, ascending aortic flow averages 62.5 mL/cycle in the CS group, higher than the sham and CC groups (p= <0.05). Both CS and CC animals have increased post-stenotic velocities (>1.5m/s), compared to the sham aorta (p= 0.01). All coarcted aortas, both CS and CC, exhibited helical flow and post-stenotic dilatation. Conclusions: No hemodynamic differences were seen with CMR, reflecting minimal adverse myocardial remodeling. 4D MRI data revealed locational differences in flow, velocity and collateralization. In conclusion, this work illustrates a successful longitudinal large animal model of COA and validates advanced imaging methods in the study of vascular hemodynamics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".