Virtual Cath Lab: Versatile Open-Source Simulator for Education and Procedural Planning in Congenital Heart Interventions
Bibliographic record
Abstract
Background: Transcatheter cardiac interventions in congenital heart disease require a precise understanding of 3-dimensional (3D) anatomical structures represented through projectional angiograms. However, intraprocedural optimization of angiograms is limited by the need to reduce exposure to radiation and nephrogenic contrast. Preprocedural optimization using 3D images has the potential to improve patient outcomes and trainee education. We sought to simulate fluoroscopic projections from 3D computed tomography images within an integrated procedural planning framework with the goal of informing training and the planning of complex interventions. Methods: We developed the Virtual Cath Lab simulator in SlicerHeart to generate fluoroscopic projections from cross-sectional 3D images contextualized in a realistic biplane C-arm model. Segmented images were used to simulate angiograms. Simulated projections were compared to actual angiograms obtained in the catheterization laboratory to assess realism and accuracy. Results: The Virtual Cath Lab allowed realistic movement of a C-arm model in synchrony with the generation of realistic fluoroscopic projections. Seventeen subjects were modeled (10 ductus arteriosus stents, 4 transcatheter pulmonary valve replacement, 1 tetralogy of Fallot with major aortopulmonary collateral arteries, 1 aortopulmonary fistula, and 1 reverse Potts shunt). The simulator successfully generated fluoroscopic projections of each subject, rapidly producing clear and anatomically accurate images, suitable for procedural planning in all cases. Conclusions: We report the development and application of an open-source, freely available, biplane fluoroscopy simulator based on computed tomography images. Integrated visualization of complex vascular anatomy prior to catheterization may facilitate optimization of fluoroscopic angles and procedural decision-making while also supporting education. Further studies are needed to demonstrate the clinical and educational benefits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".