1. Articles from Pengfei Dong

    1-2 of 2
    1. Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery

      Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery

      In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on optical coherence tomography images. Following FE simulation, eight geometrical features in each of 120 cross sections in the pre-stenting artery model, as well as the corresponding post-stenting lumen area, were extracted for training and testing the ML models. A linear regression model and a support vector regression (SVR) model with three different kernels (linear, polynomial, and radial basis function kernels) were adopted in ...

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    2. Optical Coherence Tomography-Based Modeling of Stent Deployment in Heavily Calcified Coronary Lesion

      Optical Coherence Tomography-Based Modeling of Stent Deployment in Heavily Calcified Coronary Lesion

      In this work, a heavily calcified coronary artery model was reconstructed from optical coherence tomography (OCT) images to investigate the impact of calcification characteristics on stenting outcomes. The calcification was quantified at various cross sections in terms of angle, maximum thickness, and area. The stent deployment procedure, including the crimping, expansion, and recoil, was implemented. The influence of calcification characteristics on stent expansion, malapposition, and lesion mechanics was characterized. Results have shown that the minimal lumen area following stenting occurred at the cross section with the greatest calcification angle. The calcification angle constricted the stretchability of the lesion and thus ...

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    1-2 of 2
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    1. (1 articles) Case Western Reserve University
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