Related Experiment Video
Updated: Jun 23, 2025
Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Non-calcified plaque-based coronary stenosis grading in contrast enhanced CT
Muhammad Moazzam Jawaid1, Sanam Narejo2, Farhan Riaz1
1University of Lincoln, UK.
Insights
This study introduces a novel CNN-based model for accurate coronary stenosis grading. The model effectively quantifies non-calcified plaques, achieving expert-level precision in assessing coronary artery disease severity.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Coronary heart disease mortality necessitates advanced non-invasive cardiac diagnostic methods.
- Computed tomography (CT) and magnetic resonance imaging (MRI) face challenges in assessing non-calcified plaques due to intensity ambiguities.
- Accurate stenosis computation is crucial for effective clinical assessment and patient management.
Purpose of the Study:
- To develop and validate a computational approach for quantifying coronary stenosis.
- To investigate cross-sectional lumen behavior for improved plaque assessment.
- To grade stenosis severity using a CNN-based model for clinical application.
Main Methods:
- Reconstruction of segment-wise orthogonal volumes from segmented coronary trees in 3D.
- Application of a CNN-based model for cross-sectional volumetric data analysis and stenosis grading.
- Validation of stenosis grading by comparing vessel-wall thickness and lumen area obstruction with expert annotations.
Main Results:
- The proposed method achieved an overall accuracy of 83% in quantifying coronary vessel stenosis.
- Individual stenosis grading accuracies were 86% (severe), 90% (moderate), and 79% (mild).
- Lumen area analysis showed a mean deviation of only 5% compared to expert quantification.
Conclusions:
- The developed model demonstrates the capability to grade vessel stenosis with high accuracy and precision.
- The model's performance is comparable to that of human experts in stenosis assessment.
- This approach offers a promising tool for objective and reliable coronary stenosis evaluation.
Background:
The high mortality rate associated with coronary heart disease has led to state-of-the-art non-invasive methods for cardiac diagnosis including computed tomography and magnetic resonance imaging. However, stenosis computation and clinical assessment of non-calcified plaques has been very challenging due to their ambiguous intensity response in CT i.e. a significant overlap with surrounding muscle tissues and blood. Accordingly, this research presents an approach for computation of coronary stenosis by investigating cross-sectional lumen behaviour along the length of 3D coronary segments.
Methods:
Non-calcified plaques are characterized by comparatively lower-intensity values with respect to the surrounding. Accordingly, segment-wise orthogonal volume was reconstructed in 3D space using the segmented coronary tree. Subsequently, the cross sectional volumetric data was investigated using proposed CNN-based plaque quantification model and subsequent stenosis grading in clinical context was performed. In the last step, plaque-affected orthogonal volume was further investigated by comparing vessel-wall thickness and lumen area obstruction w.r.t. expert-based annotations to validate the stenosis grading performance of model.
Results:
The experimental data consists of clinical CT images obtained from the Rotterdam CT repository leading to 600 coronary segments and subsequent 15786 cross-sectional images. According to the results, the proposed method quantified coronary vessel stenosis i.e. severity of the non-calcified plaque with an overall accuracy of 83%. Moreover, for individual grading, the proposed model show promising results with accuracy equal to 86%, 90% and 79% respectively for severe, moderate and mild stenosis. The stenosis grading performance of the proposed model was further validated by performing lumen-area versus wall-thickness analysis as per annotations of manual experts. The statistical results for lumen area analysis precisely correlates with the quantification performance of the model with a mean deviation of 5% only.
Conclusion:
The overall results demonstrates capability of the proposed model to grade the vessel stenosis with reasonable accuracy and precision equivalent to human experts.
More Related Videos
06:57Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
Published on: September 22, 2023
08:02Author Spotlight: Enhanced Quantification of Cardiovascular Calcification Progression for Longitudinal Micro PET/CT Studies in Small Research Animals
Published on: November 15, 2024
