Computational framework for the generation of one‐dimensional vascular models accounting for uncertainty in networks extracted from medical images

作者：Michelle A. Bartolo, Alyssa Taylor-LaPole, Darsh Gandhi, Alexandria Johnson, Yaqi Li, Emma Slack, Isaiah Stevens, Zachary G. Turner, Justin Weigand, Charles Puelz, Dirk Husmeier, Mette S. Olufsen · 发表于：The Journal of Physiology · 年份：2024 · DOI：10.1113/jp286193 · 被引用次数：6 · 研究领域：Cardiovascular Health and Disease Prevention、Cerebrovascular and Carotid Artery Diseases、Medical Image Segmentation Techniques

One-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 in in vivo imaging 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-...