Automatic Coronary Artery Plaque Quantification and CAD-RADS Prediction Using Mesh Priors
Insights
This study introduces a novel method for analyzing coronary artery disease (CAD) using coronary CT angiography (CCTA). The approach directly infers 3D surface meshes of coronary arteries, improving CAD-RADS scoring accuracy for better cardiovascular risk assessment.
Area of Science:
- Medical Imaging
- Cardiovascular Research
- Computational Anatomy
Background:
- Coronary artery disease (CAD) is a major global health concern.
- Coronary CT angiography (CCTA) is crucial for diagnosing CAD and assessing cardiovascular event risk.
- Current analysis methods for CCTA, including CAD-RADS scoring, require accurate segmentation of coronary arteries and atherosclerotic plaque.
Purpose of the Study:
- To develop a novel method for directly inferring surface meshes of coronary artery lumen and plaque from CCTA.
- To utilize these surface meshes for automated CAD-RADS categorization.
- To evaluate the accuracy and feasibility of the proposed mesh-inference method for clinical application.
Main Methods:
- A novel approach was developed to directly infer surface meshes for coronary artery lumen and plaque using a centerline prior.
- The method was trained and evaluated on 2407 CCTA scans.
- Downstream task of CAD-RADS scoring was performed using the inferred meshes.
Main Results:
- The method achieved high lesion-wise volume intraclass correlation coefficients: 0.98 (calcified), 0.79 (non-calcified), and 0.85 (total plaque).
- Patient-level CAD-RADS categorization achieved a linearly weighted kappa (κ) of 0.75 on a 300-scan test set.
- Cross-institutional validation on 658 scans yielded a κ of 0.71, demonstrating robustness.
Conclusions:
- Direct inference of coronary artery lumen and plaque surface meshes from CCTA is feasible.
- This mesh-based approach enables automated and accurate CAD-RADS categorization.
- The method holds potential for improving clinical decision-making in patients with suspected CAD.
Abstract:
Coronary artery disease (CAD) remains the leading cause of death worldwide. Patients with suspected CAD undergo coronary CT angiography (CCTA) to evaluate the risk of cardiovascular events and determine the treatment. Clinical analysis of coronary arteries in CCTA comprises the identification of atherosclerotic plaque, as well as the grading of any coronary artery stenosis typically obtained through the CAD-Reporting and Data System (CAD-RADS). This requires analysis of the coronary lumen and plaque. While voxel-wise segmentation is a commonly used approach in various segmentation tasks, it does not guarantee topologically plausible shapes. To address this, in this work, we propose to directly infer surface meshes for coronary artery lumen and plaque based on a centerline prior and use it in the downstream task of CAD-RADS scoring. The method is developed and evaluated using a total of 2407 CCTA scans. Our method achieved lesion-wise volume intraclass correlation coefficients of 0.98, 0.79, and 0.85 for calcified, non-calcified, and total plaque volume respectively. Patient-level CAD-RADS categorization was evaluated on a representative hold-out test set of 300 scans, for which the achieved linearly weighted kappa ( κ ) was 0.75. CAD-RADS categorization on the set of 658 scans from another hospital and scanner led to a κ of 0.71. The results demonstrate that direct inference of coronary artery meshes for lumen and plaque is feasible, and allows for the automated prediction of routinely performed CAD-RADS categorization.
More Related Videos
Related Concept Videos
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:30Coronary Artery Disease I: Introduction