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

lyu, m.; liang, c.; zhang, x.; li, q.; ryo, t.; ventikos, y.; chen, d.

2024-07-31 bioengineering
10.1101/2024.07.29.605713 bioRxiv
Show 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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