Preclinical validation of the advection diffusion flow estimation method using computational patient specific...

Area of Science:

• Cardiovascular Imaging
• Medical Physics
• Computational Fluid Dynamics

Background:

• Coronary computed tomography angiography (CCTA) lacks blood flow quantification capabilities for coronary artery disease (CAD).
• Existing numerical methods for blood flow computation require difficult-to-estimate patient-specific boundary conditions.
• Accurate blood flow assessment is crucial for evaluating CAD severity and impact.

Purpose of the Study:

• To introduce and validate a novel noninvasive flow estimation method, Advection Diffusion Flow Estimation (ADFE).
• To compute coronary artery flow from CCTA data for use as boundary conditions in numerical models.
• To enhance the accuracy of blood flow quantification in coronary arteries.

Main Methods:

• ADFE estimates flow by analyzing image contrast variations along the coronary artery tree.
• Validation employed patient-specific software phantoms with simulated contrast transport.
• Ground truth simulations utilized a spectral element method solver on 10 CCTA datasets.

Main Results:

• The ADFE method demonstrated a high correlation coefficient ($$) between estimated and ground truth flow.
• ADFE achieved a low average relative error of $$.
• Compared to the TAFE method (correlation $$, error $$), ADFE shows superior performance.

Conclusions:

• ADFE is a promising noninvasive method for estimating coronary artery blood flow from CCTA.
• The technique has the potential to significantly improve the quantification of blood flow derived from CCTA.
• Accurate flow boundary conditions derived from ADFE can enhance numerical models for CAD assessment.