A Novel Computational Pre-Procedural Planning Model for Coronary Interventions Based on Coronary CT Angiography
Mengzhe LyuiD, Ryo ToriiiD, Ce LiangiD, Xuehuan ZhangiD, Xifu WangiD, Qiaoqiao LiiD, Yiannis VentikosiD, Duanduan CheniD
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 mm2 and post-CSA is 17.91 {+/-} 4.84 mm2 (mean difference: -1.55 {+/-} 1.89 mm2; limits of agreement: -5.22 to 2.12), the predicted centreline curvature of stented region is 0.15 {+/-} 0.04 mm{square}1 and post-PCI centreline curvature is 0.17 {+/-} 0.03 mm{square}1 (mean difference: -0.02 {+/-} 0.06 mm{square}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. HighlightsO_LIPresent a computational pre-procedural planning model for coronary interventions. C_LIO_LIDevelop a computational framework to predict post-PCI FFR. C_LIO_LIValidation of the model with post-PCI CT angiography datasets. C_LIO_LIThe proposed model has the potential to optimize PCI pre-procedural planning. C_LI
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