A personalized computational framework for the diagnosis of cardiac perfusion defects

• Published in: Computer methods and programs in biomedicine Vol. 271; p. 108990
• Main Authors: Criseo, Elisabetta, Baggiano, Andrea, Montino Pelagi, Giovanni, Nannini, Guido, Cusumano, Viola, Redaelli, Alberto, Pontone, Gianluca, Vergara, Christian
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.11.2025
• Subjects: Aged; Algorithms; Computational fluid dynamics; Computer Simulation; Coronary artery disease; Coronary Artery Disease - diagnosis; Coronary Artery Disease - diagnostic imaging; Coronary Artery Disease - physiopathology; Coronary Circulation; Coronary Vessels - diagnostic imaging; Female; Humans; Male; Middle Aged; Myocardial blood flow; Myocardial Perfusion Imaging; Reproducibility of Results; Tomography, X-Ray Computed; Computational fluid dynamics; Coronary artery disease; Myocardial blood flow
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2025.108990

Myocardial Blood Flow (MBF) is a key indicator of myocardial perfusion, typically assessed through additional clinical tests like dynamic CT perfusion under stress. This study introduces a computational framework designed to enhance coronary artery disease diagnosis by predicting MBF using data from routine CT images and clinical measurements. The computational framework employs AI methods to reconstruct coronary and myocardial geometries and integrates a computational model, featuring 3D coronary arteries and a three-compartment myocardial model, blindly calibrated with data from six representative patients. Validation on 28 additional patients showed MBF predictions consistent with experimental and clinical measurements. Confusion matrix analysis assessed the twin’s ability to classify pathological (averaged MBF < 240 ml/min/100 g) versus healthy perfusion regions, yielding a recall of 0.81, with precision of 0.68 and accuracy at 0.7. This work represents the first attempt to predict and validate MBF on such a large cohort, paving the way for future clinical applications. •Computational myocardial perfusion prediction to overcome stress-CTP limitations.•Adapted an effective AI-based segmentation strategy to cardiac perfusion prediction.•Blindly calibrated a computational model for myocardial perfusion.•Tested a computational framework to study cardiac perfusion on the largest cohort yet.