Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images
Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images
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DOI：
10.1113/jp286193
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发表时间：
2024-07-29
影响因子：
5.5
通讯作者：
Olufsen，Mette S.
中科院分区：
文献类型：
--
作者：
Bartolo，Michelle A.;Taylor-LaPole，Alyssa M.;Olufsen，Mette S.
摘要一维（1D）心血管模型提供了一种非侵入性方法来回答医学问题，包括波反射、剪切应力、功能性血流储备、血管阻力和顺应性的预测。这种模型类型可以通过求解从医学图像中提取的几何网络中的一维流体动力学方程来预测患者特定的结果。然而，体内成像固有的不确定性引入了网络大小和血管尺寸的可变性，影响了血液动力学预测。了解图像衍生属性变化的影响对于评估模型预测的保真度至关重要。有许多程序可以渲染三维表面并构建血管中心线。尽管如此，在考虑数据不确定性的同时，还没有确切的方法从中心线生成维管树。这项研究引入了一种创新框架，采用统计变化点分析来生成标记树，对医学图像中的血管尺寸及其相关的不确定性进行编码。为了测试这个框架，我们探讨了全身动脉网络和肺动脉网络中一维血流动力学预测的不确定性的影响。模拟探索因血管尺寸和分段变化而导致的血流动力学变化；后者是通过分析同一图像的多个分割来实现的。结果证明了在生成高保真患者特定血液动力学模型时准确定义血管半径和长度的重要性。要点本研究引入了从医学图像生成标记有向树的新算法，重点关注使用变化点的准确连接节点放置和半径提取，以在预期测量误差内提供具有不确定性的血液动力学预测。几何特征，例如血管尺寸（长度和半径）和网络尺寸，显着影响肺动脉和主动脉网络中的压力和流量预测。将网络标准化为一致的血管数量对于有意义的比较和减少血液动力学不确定性至关重要。变化点对于理解血管数据中的结构转变很有价值，提供了一种自动且有效的方法来检测血管特征的变化并确保可靠地提取代表性血管半径。
AbstractOne‐dimensional (1D) cardiovascular models offer a non‐invasive method to answer medical questions, including predictions of wave‐reflection, shear stress, functional flow reserve, vascular resistance and compliance. This model type can predict patient‐specific outcomes by solving 1D fluid dynamics equations in geometric networks extracted from medical images. However, the inherent uncertainty inin vivoimaging introduces variability in network size and vessel dimensions, affecting haemodynamic predictions. Understanding the influence of variation in image‐derived properties is essential to assess the fidelity of model predictions. Numerous programs exist to render three‐dimensional surfaces and construct vessel centrelines. Still, there is no exact way to generate vascular trees from the centrelines while accounting for uncertainty in data. This study introduces an innovative framework employing statistical change point analysis to generate labelled trees that encode vessel dimensions and their associated uncertainty from medical images. To test this framework, we explore the impact of uncertainty in 1D haemodynamic predictions in a systemic and pulmonary arterial network. Simulations explore haemodynamic variations resulting from changes in vessel dimensions and segmentation; the latter is achieved by analysing multiple segmentations of the same images. Results demonstrate the importance of accurately defining vessel radii and lengths when generating high‐fidelity patient‐specific haemodynamics models.Key pointsThis study introduces novel algorithms for generating labelled directed trees from medical images, focusing on accurate junction node placement and radius extraction using change points to provide haemodynamic predictions with uncertainty within expected measurement error.Geometric features, such as vessel dimension (length and radius) and network size, significantly impact pressure and flow predictions in both pulmonary and aortic arterial networks.Standardizing networks to a consistent number of vessels is crucial for meaningful comparisons and decreases haemodynamic uncertainty.Change points are valuable to understanding structural transitions in vascular data, providing an automated and efficient way to detect shifts in vessel characteristics and ensure reliable extraction of representative vessel radii.