A personalized computational framework for the diagnosis of cardiac perfusion defects.

BACKGROUND AND

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

This work represents the first attempt to predict and validate MBF on such a large cohort, paving the way for future clinical applications.