Bi-VesTreeFormer: A bidirectional topology-aware transformer framework for coronary vFFR estimation

Area of Science:

• Cardiovascular Imaging and Diagnostics
• Medical Artificial Intelligence
• Computational Physiology

Background:

• Fractional Flow Reserve (FFR) is the standard for assessing coronary artery stenosis severity.
• Traditional FFR requires invasive procedures, increasing patient risks and costs.
• Non-invasive virtual FFR (vFFR) methods are computationally intensive and struggle with complex vessel geometries.

Purpose of the Study:

• To develop a novel, efficient, and automated framework for non-invasive coronary vFFR estimation.
• To overcome limitations of existing vFFR methods, including manual feature engineering and poor handling of long-range dependencies.

Main Methods:

• Introduction of a bidirectional topology-aware transformer network (Bi-VesTreeFormer) for automated feature extraction and global dependency analysis.
• Development of a contextual vFFR decoder to map FFR values and correlate them across vessel branches.
• Training and validation using real patient FFR data (43 patients) and simulated coronary artery data (15,000 cases).

Main Results:

• The proposed Bi-VesTreeFormer framework achieved a root mean square error (RMSE) of 0.038 for simulated data and 0.048 for real-world data.
• The method demonstrated superior performance compared to existing state-of-the-art vFFR estimation techniques.
• The framework successfully automated topological feature extraction and captured global dependencies within the coronary vasculature.

Conclusions:

• The novel Bi-VesTreeFormer framework offers a highly accurate and efficient solution for non-invasive vFFR estimation.
• This deep learning approach reduces reliance on manual preprocessing and improves the analysis of complex coronary artery structures.
• The findings suggest a significant advancement in computational cardiology, potentially improving diagnosis and treatment planning for coronary artery stenosis.