Clinical Risk-Aware Multi-Level Grading for Coronary Artery Stenosis through Curved Feature Reconstruction

2026-06-29

AI Summary

This study addresses the challenges in existing methods for multi-class coronary artery stenosis grading using fused CCTA and 3D SCPR images, which suffer from inaccurate alignment and neglect of clinically relevant risk differences across stenosis severity levels. To overcome these limitations, the authors propose a Curved Feature Reconstruction (CFR) module that leverages geometric priors of vessel centerlines to achieve point-wise alignment and feature fusion between the two modalities. Additionally, a Clinical Risk-aware (CR) loss function is introduced to explicitly embed differential clinical risks into the training process. Integrated within a deep multi-class classification network, the proposed approach significantly outperforms current methods on an internal dataset. Ablation studies confirm the effectiveness of both the CFR module and the CR loss, demonstrating improved alignment with clinical requirements in stenosis grading.

Abstract

Developing a multi-level grading model for coronary artery stenosis holds great clinical significance for the diagnosis of coronary artery disease. However, designing an effective multi-level deep learning algorithm faces significant challenges. Specifically, utilizing CCTA or 3D SCPR images alone presents inherent shortcomings: CCTA images are difficult to analyze due to the tortuous paths of blood vessels, while 3D SCPR images are prone to abnormal distortions that hinder accurate grading. Furthermore, different stenosis grades are associated with varying clinical risks, and incorporating this association into the algorithm is non-trivial. To address the former problems, we propose the Curved Feature Reconstruction (CFR) module, which uses vessel curves as prior and employs a point-by-point correspondence strategy to precisely align and fuse features from both 3D SCPR and CCTA images. Meanwhile, a Clinical Risk-Aware (CR) Loss is employed to introduce clinical risk relevance into the network training so that the algorithm can better align with the clinical diagnosis. The experimental results on a in-house dataset reveal that our approach significantly outperforms other methods, and several ablation studies also demonstrate the effectiveness of our proposed designs.

Problem

• coronary artery stenosis
• multi-level grading
• clinical risk
• CCTA
• 3D SCPR

Innovation

• Curved Feature Reconstruction
• Clinical Risk-Aware Loss
• Multi-Level Grading
• Coronary Artery Stenosis
• Feature Fusion