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Updated: Aug 23, 2025
Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
Published on: January 15, 2022
Rapid lipid-laden plaque identification in intravascular optical coherence tomography imaging based on time-series
Jose J Rico-Jimenez1, Javier A Jo2
1Texas A&M University, Department of Biomedical Engineering, College Station, Texas, United States, United States.
Insights
A deep learning method rapidly identifies lipid-laden plaques from intravascular optical coherence tomography (IV-OCT) images. This high-throughput approach aids in faster screening and decision-making during coronary interventions.
Area of Science:
- Cardiovascular Medicine
- Medical Imaging
- Artificial Intelligence
Background:
- Coronary heart disease, driven by atherosclerosis, is a leading cause of mortality.
- Atherosclerotic plaques, particularly lipid-laden ones, pose a risk for heart attack and stroke.
- Intravascular optical coherence tomography (IV-OCT) aids plaque assessment but requires time-consuming manual interpretation.
Purpose of the Study:
- To develop a high-throughput method for identifying lipid-laden atherosclerotic plaques.
- To assist in vivo IV-OCT imaging for faster screening and guided decision-making during percutaneous coronary interventions.
Main Methods:
- An A-line-wise classification methodology utilizing time-series deep learning was developed.
- The deep learning model was trained and validated on IV-OCT images from 98 artery sections.
- Ground truth for training and validation was established through expert physician visual evaluation of IV-OCT images.
Main Results:
- The deep learning method achieved an accuracy of 89.6%, sensitivity of 83.6%, and specificity of 91.1%.
- The methodology demonstrated potential for high-throughput identification of lipid-laden plaques, exceeding 100 B-scans/s.
- Automated tissue characterization enables video-rate atherosclerotic plaque assessment.
Conclusions:
- The developed deep learning method shows promise for high-throughput atherosclerotic plaque assessment.
- Automated plaque characterization can significantly enhance in vivo IV-OCT imaging.
- This approach can expedite screening and support clinical decision-making in interventional cardiology.
Significance:
Coronary heart disease has the highest rate of death and morbidity in the Western world. Atherosclerosis is an asymptomatic condition that is considered the primary cause of cardiovascular diseases. The accumulation of low-density lipoprotein triggers an inflammatory process in focal areas of arteries, which leads to the formation of plaques. Lipid-laden plaques containing a necrotic core may eventually rupture, causing heart attack and stroke. Lately, intravascular optical coherence tomography (IV-OCT) imaging has been used for plaque assessment. The interpretation of the IV-OCT images is performed visually, which is burdensome and requires highly trained physicians for accurate plaque identification.
Aim:
Our study aims to provide high throughput lipid-laden plaque identification that can assist in vivo imaging by offering faster screening and guided decision making during percutaneous coronary interventions.
Approach:
An A-line-wise classification methodology based on time-series deep learning is presented to fulfill this aim. The classifier was trained and validated with a database consisting of IV-OCT images of 98 artery sections. A trained physician with expertise in the analysis of IV-OCT imaging provided the visual evaluation of the database that was used as ground truth for training and validation.
Results:
This method showed an accuracy, sensitivity, and specificity of 89.6%, 83.6%, and 91.1%, respectively. This deep learning methodology has the potential to increase the speed of lipid-laden plaques identification to provide a high throughput of more than 100 B-scans/s.
Conclusions:
These encouraging results suggest that this method will allow for high throughput video-rate atherosclerotic plaque assessment through automated tissue characterization for in vivo imaging by providing faster screening to assist in guided decision making during percutaneous coronary interventions.
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