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Development and Initial Validation of the Novel Computational Method for Dynamic Intracardiac Blood Flow Evaluation
Dmytro Volkov1,2, Dmytro Skoryi3, Bogdan Batsak4
1Department of Electrophysiology, Texas Cardiac Arrhythmia Institute, St. David's Medical Center, Austin, TX 78705, USA.
Diagnostics (Basel, Switzerland)
|May 13, 2026
Summary
A new Python program visualizes intracardiac blood flow, introducing turbulence index (TI) and blood mobility fraction (BMF) as potential cardiac biomarkers. This tool aids in preliminary flow-based cardiac evaluation.
Area of Science:
- Medical Imaging
- Computational Fluid Dynamics
- Cardiology
Background:
- Intracardiac blood flow assessment is crucial for cardiac evaluation.
- Current methods for flow analysis are limited.
- Developing novel quantitative parameters is essential for advancing cardiac diagnostics.
Purpose of the Study:
- To develop and evaluate a Python-based program for dynamic intracardiac blood flow visualization.
- To extract novel quantitative parameters from blood flow data.
- To establish a foundation for future flow-based cardiac evaluation.
Main Methods:
- Exploration across five imaging modalities (angiography, MRI, ICE, TEE, TTE).
- Utilized standard diagnostic hardware and ECG-gated ICE DICOM images.
- Preliminary testing on sixteen patients undergoing AF ablation.
Main Results:
- Dynamic full-chamber flow visualizations were generated.
- Automated computation of turbulence index (TI) and blood mobility fraction (BMF).
- Distinct flow patterns observed between sinus rhythm and atrial fibrillation; outputs exportable for AI analysis.
Conclusions:
- Proof-of-concept demonstrates feasibility for routine intracardiac flow assessments.
- Introduced TI and BMF as potential flow-based biomarkers.
- Further validation is required for prognostic use.
