A Novel Computational Pre-Procedural Planning Model for Coronary Interventions Based on Coronary CT Angiography

Abstract In percutaneous coronary intervention (PCI), the ability to predict post-PCI fractional flow reserve (FFR) and stented vessel informs procedural planning. However, highly precise and effective methods to quantitatively simulate coronary intervention are lacking. This study developed a validated virtual coronary intervention (VCI) technique for non-invasive physiological and anatomical assessment of PCI. In this study, patients with substantial lesions (pre-PCI FFR of less than 0.80) were enrolled. VCI framework was used to predict vessel reshape and post-PCI FFR. The accuracy of predicted post-VCI FFR, luminal cross-sectional area (CSA) and centreline curvature was validated with post-PCI computed tomography (CT) angiography datasets. Overall, 21 patients were selected for the study, of which 9 patients (9 vessels) were included in the analysis. The average time for PCI simulation was 24.92 ± 1.00 s on a single processor. The calculated post-PCI FFR was 0.92 ± 0.09 and the predicted post-VCI FFR was 0.90 ± 0.08 (mean difference: -0.02 ± 0.05 FFR unit; limits of agreement: -0.08 to 0.05). Morphologically, the predicted CSA is 16.36 ± 4.41 mm 2 and post-CSA is 17.91 ± 4.84 mm 2 (mean difference: -1.55 ± 1.89 mm 2 ; limits of agreement: -5.22 to 2.12), the predicted centreline curvature of stented region is 0.15 ± 0.04 mm□ 1 and post-PCI centreline curvature is 0.17 ± 0.03 mm□ 1 (mean difference: -0.02 ± 0.06 mm□ 1 ; limits of agreement: -0.12 to 0.09). The proposed VCI technique achieves non-invasive pre-procedural anatomical and physiological assessment of coronary intervention. The proposed model has the potential to optimize PCI pre-procedural planning and improve the safety and efficiency of PCI. Highlights Present a computational pre-procedural planning model for coronary interventions. Develop a computational framework to predict post-PCI FFR. Validation of the model with post-PCI CT angiography datasets. The proposed model has the potential to optimize PCI pre-procedural planning.