arXiv:2512.10765v1 Announce Type: new 
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.

تم تطوير خط أنابيب آلي جديد لتحسين تشخيص مرض الشريان التاجي (CAD) من خلال التنبؤ بتوزيعات ضغط الدم باستخدام تصوير الأوعية التاجية بواسطة الأشعة المقطعية (CCTA). يستخدم هذا النظام محاكاة الديناميكا الهوائية الحاسوبية (CFD) لتوليد بيانات تدريب متسقة، مما يقلل من العبء اليدوي المرتبط بالطرق التقليدية.

Se ha desarrollado un nuevo pipeline automatizado para mejorar el diagnóstico de la enfermedad arterial coronaria (CAD) mediante la predicción de distribuciones de presión sanguínea utilizando la angiografía por tomografía computarizada coronaria (CCTA). Este sistema utiliza simulaciones de dinámica de fluidos computacional (CFD) para generar datos de entrenamiento consistentes, reduciendo así la carga de trabajo manual asociada con los métodos tradicionales.

Un nouveau pipeline automatisé a été développé pour améliorer le diagnostic de la maladie coronarienne (CAD) en prédisant les distributions de pression sanguine à l'aide de l'angiographie par tomodensitométrie coronarienne (CCTA). Ce système utilise des simulations de dynamique des fluides computationnelle (CFD) pour générer des données d'entraînement cohérentes tout en réduisant la charge de travail manuelle associée aux méthodes traditionnelles.

A new automated pipeline has been developed to enhance the diagnosis of coronary artery disease (CAD) by predicting blood pressure distributions using coronary computed tomography angiography (CCTA). This system utilizes computational fluid dynamics (CFD) simulations to generate consistent training data while reducing the manual workload associated with traditional methods.

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

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

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