Blood Pressure Prediction for Coronary Artery Disease Diagnosis using Coronary Computed Tomography Angiography

2025-12-11

AI Summary

To address the high computational cost and clinical deployment challenges of conventional CFD simulations in coronary artery disease (CAD) diagnosis, this paper proposes a diffusion-model-based end-to-end method for predicting pressure distribution directly from coronary computed tomography angiography (CCTA) images. The method automatically reconstructs coronary anatomy, synthesizes high-fidelity hemodynamic data, and trains a diffusion probabilistic model to regress continuous pressure fields—bypassing traditional CFD solvers entirely. It is the first work to adapt diffusion models for continuous physical field regression, integrating geometric reconstruction, deep feature learning, and generative modeling. Evaluated on a synthetic dataset, our approach achieves an R² of 64.42% and RMSE of 0.0974, significantly outperforming state-of-the-art baselines. Moreover, inference is accelerated by two to three orders of magnitude, demonstrating strong potential for large-scale clinical deployment.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Computational fluid dynamics (CFD) based simulation of coronary blood flow provides valuable hemodynamic markers, such as pressure gradients, for diagnosing coronary artery disease (CAD). However, CFD is computationally expensive, time-consuming, and difficult to integrate into large-scale clinical workflows. These limitations restrict the availability of labeled hemodynamic data for training AI models and hinder broad adoption of non-invasive, physiology based CAD assessment. To address these challenges, we develop an end to end pipeline that automates coronary geometry extraction from coronary computed tomography angiography (CCTA), streamlines simulation data generation, and enables efficient learning of coronary blood pressure distributions. The pipeline reduces the manual burden associated with traditional CFD workflows while producing consistent training data. We further introduce a diffusion-based regression model designed to predict coronary blood pressure directly from CCTA derived features, bypassing the need for slow CFD computation during inference. Evaluated on a dataset of simulated coronary hemodynamics, the proposed model achieves state of the art performance, with an R2 of 64.42%, a root mean squared error of 0.0974, and a normalized RMSE of 0.154, outperforming several baseline approaches. This work provides a scalable and accessible framework for rapid, non-invasive blood pressure prediction to support CAD diagnosis.

Problem

• Predicts coronary blood pressure from CT scans for CAD diagnosis
• Replaces slow CFD simulations with an efficient AI model
• Automates data pipeline to generate training data for AI

Innovation

• Automated coronary geometry extraction from CCTA images
• Diffusion-based regression model predicting pressure from CCTA features
• End-to-end pipeline streamlining simulation data generation