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SYSTEMS AND METHODS FOR ESTIMATION OF BLOOD FLOW CHARACTERISTICS USING REDUCED ORDER MODEL AND/OR MACHINE LEARNING

机译:降阶模型和/或机器学习估计血液流动特性的系统和方法

摘要

Systems and methods are disclosed for determining blood flow characteristics of a patient. One method includes: receiving, in an electronic storage medium, patient-specific image data of at least a portion of vasculature of the patient having geometric features at one or more points; generating a patient-specific reduced order mode! from the received image data, the patient-specific reduced order model comprising estimates of impedance values and a simplification of the geometric features at the one or more points of the vasculature of the patient; creating a feature vector comprising the estimates of impedance values and geometric features for each of the one or more points of the patient-specific reduced order model; and determining blood flow characteristics at the one or more points of the patient- specific reduced order model using a machine learning algorithm trained to predict blood flow characteristics based on the created feature vectors at the one or more points.
机译:公开了用于确定患者的血流特征的系统和方法。一种方法包括:在电子存储介质中接收患者的至少一部分脉管系统的特定于患者的图像数据,该图像在一个或多个点具有几何特征;生成特定于患者的减价订单模式!从接收的图像数据中,特定于患者的降阶模型包括对阻抗值的估计以及对患者脉管系统的一个或多个点处的几何特征的简化;创建特征向量,该特征向量包括针对患者特定降阶模型的一个或多个点中的每个点的阻抗值和几何特征的估计值;以及使用机器学习算法确定患者特定降阶模型的一个或多个点的血流特征,该算法被训练为基于在一个或多个点处创建的特征向量来预测血流特征。

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