Background/Objectives: To develop and preliminarily evaluate a practical Python-based program for dynamic intracardiac blood flow visualization and the extraction of new quantitative parameters, serving as an initial step toward future flow-based cardiac evaluation. Methods: The method was technically explored across five imaging modalities (angiography, MRI, ICE, TEE, TTE) using standard diagnostic hardware. Preliminary testing used ECG-gated ICE DICOM images from sixteen patients undergoing first-time AF ablation. Results: The program produced dynamic full-chamber flow visualizations and automatically computed two image-derived surrogate markers of flow-pattern behavior, the *turbulence index (TI)* and *blood mobility fraction (BMF)*, across cardiac cycles. Distinct preliminary flow patterns were observed between sinus rhythm and atrial fibrillation. Outputs are exportable for AI analysis. Conclusions: This proof-of-concept approach demonstrates feasibility for routine intracardiac flow assessments, and introduces TI and BMF as potential flow-based biomarkers for future prognostic use after additional validation.
Volkov et al. (2026) studied this question.