Quantification of myocardial ischemia and subtended myocardial mass at adenosine stress cardiac computed tomography: a feasibility study

Coronary CT Angiography in the Cath Lab: Leveraging Artificial Intelligence to Plan and Guide Percutaneous Coronary Intervention

The role of coronary CT angiography for the diagnosis and risk stratification of coronary artery disease is well established. However, its potential beyond the diagnostic phase remains to be determined. The current review focuses on the insights that coronary CT angiography can provide when planning and performing percutaneous coronary interventions. We describe a novel approach incorporating anatomical and functional pre-procedural planning enhanced by artificial intelligence, computational physiology and online 3D CT guidance for percutaneous coronary interventions. This strategy allows the individualisation of patient selection, optimisation of the revascularisation strategy and effective use of resources.

Correlation between quantification of myocardial area at risk and ischemic burden at cardiac computed tomography

Purpose

This study aims to investigate the correlation between myocardial area at risk at coronary computed tomography angiography (CCTA) and the ischemic burden derived from myocardial computed tomography perfusion (CTP) by using the 17-segment model.

Methods

Forty-two patients with chest pain complaints who underwent a combined CCTA and CTP protocol were identified. Patients with reversible ischemia at CTP and at least one stenosis of ≥ 50% at CCTA were selected. Myocardial area at risk was calculated using a Voronoi-based segmentation algorithm at CCTA and was defined as the sum of all territories related to a ≥ 50% stenosis as a percentage of the total left ventricular (LV) mass. The latter was calculated using LV contours which were automatically drawn using a machine learning algorithm. Subsequently, the ischemic burden was defined as the number of segments demonstrating relative hypoperfusion as a percentage of the total amount of segments (=17). Finally, correlations were tested between the myocardial area at risk and the ischemic burden using Pearson’s correlation coefficient.

Results

A total of 77 coronary lesions were assessed. Average myocardial area at risk and ischemic burden for all lesions was 59% and 23%, respectively. Correlations for ≥ 50% and ≥ 70% stenosis based myocardial area at risk compared to ischemic burden were moderate (r = 0.564; p < 0.01) and good (r = 0.708; p < 0.01), respectively.

Conclusion

The relation between myocardial area at risk as calculated by using a Voronoi-based algorithm at CCTA and ischemic burden as assessed by CTP is dependent on stenosis severity.