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Related Experiment Video

Updated: May 22, 2026

Monitoring the Wall Mechanics During Stent Deployment in a Vessel

Monitoring the Wall Mechanics During Stent Deployment in a Vessel

Published on: May 8, 2012

Automatic stent strut detection in intravascular optical coherence tomographic pullback runs.

Ancong Wang¹, Jeroen Eggermont, Niels Dekker

¹LKEB-Division of Image Processing, Department of Radiology, Leiden University Medical Center, P. O. Box 9600, Leiden, The Netherlands.

The International Journal of Cardiovascular Imaging

|May 24, 2012

Summary

An automated method accurately detects stent struts in intravascular optical coherence tomography (IVOCT) images. This advancement enables precise 3D analysis of coronary stents, improving procedural optimization and follow-up evaluation.

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Published on: October 3, 2018

Area of Science:

Medical Imaging
Cardiovascular Technology
Computational Pathology

Background:

Intravascular optical coherence tomography (IVOCT) provides high-resolution imaging for coronary stent analysis.
Automatic stent strut detection is crucial for quantitative 3D analysis of IVOCT pullback runs.
Current methods face challenges with the large number of struts and varying strut statuses.

Purpose of the Study:

To develop and validate an automated method for detecting stent struts in IVOCT pullback runs.
To enable accurate quantitative 3D analysis, reconstruction, and visualization of coronary stents.
To assess the method's performance across different strut statuses and image qualities.

Main Methods:

A novel algorithm utilizing global intensity histograms and A-line intensity profiles for candidate pixel detection.
Application of Gaussian smoothing and Prewitt compass filters to identify strut trailing shadows.
Clustering of candidate pixels based on shadow information, followed by false positive removal filters (e.g., guide wire).
The method requires no prior knowledge of strut status or vessel contours.

Main Results:

Validation on 10 IVOCT pullback runs (18,311 struts) with high inter-observer agreement (95%).
Demonstrated an average sensitivity of 94% for automatic stent strut detection.
Achieved high sensitivity across different strut statuses: 91% for malapposed, 93% for apposed, and 94% for covered struts.
Robust performance irrespective of image quality (high, medium, low).

Conclusions:

The developed automated method reliably detects stent struts in IVOCT pullback runs.
The approach is robust across various strut statuses and image qualities.
Enables quantitative measurements, 3D reconstruction, and visualization for improved stent analysis and patient care.