Automated accurate lumen segmentation using L-mode interpolation for three-dimensional intravascular optical...

Area of Science:

• Cardiovascular Imaging and Physiology
• Medical Device Technology
• Computational Fluid Dynamics

Background:

• Intravascular optical coherence tomography (IVOCT) is crucial for lumen-based computational flow dynamics (CFD) analysis.
• Accurate lumen contour extraction is essential for reliable physiologic evaluations like fractional flow reserve (FFR).
• Existing methods struggle with image artifacts like guide wire shadowing and complex bifurcations.

Purpose of the Study:

• To develop an accurate and time-efficient algorithm for extracting coronary artery lumen contours from IVOCT images.
• To validate the algorithm's performance against manual segmentation and other automated methods.
• To assess the accuracy of OCT-derived FFR using the proposed lumen contour extraction method.

Main Methods:

• A novel algorithm utilizing longitudinal lumen continuity was developed to delineate contours, overcoming cross-sectional image limitations.
• The algorithm was applied to 5931 pre-intervention IVOCT images from 40 patients.
• Quantitative comparison with manual segmentation and automated methods using correlation and area ratios.

Main Results:

• The proposed algorithm demonstrated superior performance with a high correlation (R=0.988) and excellent overlapping area ratio (0.931).
• CFD simulations using OCT-derived FFR showed strong correlation with manual FFR (R=0.978).
• The method effectively handled challenging image features like intimal discontinuities and bifurcations.

Conclusions:

• The developed algorithm provides accurate and efficient lumen contour extraction from IVOCT images.
• This method enhances the reliability of CFD-based physiologic assessments, including FFR.
• The algorithm shows significant potential for clinical application in cardiovascular diagnostics.