论文标题

关于肺部肺动脉树的拓扑数据分析研究

A Topological Data Analysis Study on Murine Pulmonary Arterial Trees with Pulmonary Hypertension

论文作者

Chambers, Megan, Johnston, Natalie, Livengood, Ian, Spinelli, Miya, Sazdanovic, Radmila, Olufsen, Mette S

论文摘要

由平均肺动脉血压高于20 mmHg的肺动脉高压(pH),是一种影响肺脉管系统的心血管疾病。 pH伴随着血管重塑,其中容器变得更硬,大血管扩张,较小的血管收缩。某些类型的pH,包括缺氧诱导的pH(HPH),导致微血管稀有。这项研究的目的是分析在HPH存在下肺动脉网络形态计量学的变化。为此,我们使用拓扑数据分析(TDA)的新方法,采用持续的同源性来量化动脉网络形态计量学进行控制和高血压小鼠。这些方法用于表征从微型断层扫描(Micro-CT)图像中提取的动脉树。为了比较控制动物和高血压动物之间的结果,我们使用三种修剪算法将生成的网络归一化。概念验证的研究表明,修剪方法会影响树木的空间树统计和复杂性。结果表明,HPH树的深度较高,并且方向复杂性与分支数相关,除了血管半径修剪的树木,与对照树相比,左侧和前复杂性较低。虽然需要更多数据来就HPH对网络拓扑的整体影响得出结论,但本研究提供了一个分析生物网络拓扑的框架,并且是迈向提取相关信息以诊断和检测HPH的一步。

Pulmonary hypertension (PH), defined by a mean pulmonary arterial blood pressure above 20 mmHg, is a cardiovascular disease impacting the pulmonary vasculature. PH is accompanied by vascular remodeling, wherein vessels become stiffer, large vessels dilate, and smaller vessels constrict. Some types of PH, including hypoxia-induced PH (HPH), lead to microvascular rarefaction. The goal of this study is to analyze the change in pulmonary arterial network morphometry in the presence of HPH. To do so, we use novel methods from topological data analysis (TDA), employing persistent homology to quantify arterial network morphometry for control and hypertensive mice. These methods are used to characterize arterial trees extracted from micro-computed tomography (micro-CT) images. To compare results between control and hypertensive animals, we normalize generated networks using three pruning algorithms. This proof-of-concept study shows that the pruning methods effects the spatial tree statistics and complexities of the trees. Results show that HPH trees have higher depth and that the directional complexities correlate with branch number, except for trees pruned by vessel radius, where the left and anterior complexity are lower compared to control trees. While more data is required to make a conclusion about the overall effect of HPH on network topology, this study provides a framework for analyzing the topology of biological networks and is a step towards the extraction of relevant information for diagnosing and detecting HPH.

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