Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome.
Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome.
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DOI：
10.1007/978-3-031-23443-9_24
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发表时间：
2022
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左心发育不全综合征是一种以左心发育不完全为特征的先天性心脏病。受影响的儿童接受了一系列手术，导致三尖瓣成为唯一有功能的房室瓣。这些患者中的许多人继续出现与心力衰竭和死亡相关的并发症，例如三尖瓣关闭不全。通过更好地了解三尖瓣的几何形状和功能之间的关系，可以大大增强预测哪些患者将出现反流以及纠正手术的计划。传统分析依赖于简单的整体解剖测量，通常无法捕捉局部结构变化。最近，统计形状建模已被证明对于分析三尖瓣的几何形状非常有用。我们建议使用骨骼表示（s-reps）来对这些患者的三尖瓣小叶进行建模。与传统的基于边界的模型相比，S-reps 是一种特征更丰富的表示形式，并且已被证明在统计分析方面具有优势。不幸的是，将 s-reps 拟合到许多几何形状更加困难，这限制了其强大的分析技术的应用。我们提出了对先前 s-rep 拟合方法的扩展，该方法为难以拟合的物体（例如三尖瓣的小叶）提供了改进的模型。我们结合特定应用的解剖标志和人口信息来改善对应性。我们使用几种传统的形状分析技术来比较 s-reps 与使用 SPHARM-PDM 创建的边界表示的效率。我们观察到，主成分分析使用 s-reps 产生更紧凑的形状空间，需要更少的模式来代表 90% 的总体变异，而距离加权区分表明 s-reps 在反流较少的瓣膜和反流较多的瓣膜之间提供了更显着的分类结果。这些结果证明了使用 s-reps 来关联三尖瓣的结构和功能的能力。
Hypoplastic left heart syndrome is a congenital heart disease characterized by incomplete development of the left heart. Affected children undergo a series of operations which result in the tricuspid valve becoming the only functional atrioventricular valve. Many of these patients go on to develop complications associated with heart failure and death such as tricuspid valve regurgitation. Predicting which patients will develop regurgitation as well as planning for corrective procedures could be greatly enhanced through better understanding of the relationship between geometry and function of the tricuspid valve. Traditional analysis has relied on simple, global anatomical measures which often can not capture localized structural changes. Recently, statistical shape modeling has proven to be useful for analyzing the geometry of the tricuspid valve. We propose to use skeletal representations (s-reps) for modeling the leaflets of the tricuspid valve in these patients. S-reps are a more feature-rich representation than traditional boundary-based models and have been shown to have advantages for statistical analysis. Unfortunately, it is more difficult to fit s-reps to many geometries which limits the application of their powerful analysis techniques. We propose an extension to previous s-rep fitting approaches which yields improved models for difficult to fit objects such as the leaflets of the tricuspid valve. We incorporate application-specific anatomical landmarks and population information to improve correspondence. We use several traditional shape analysis techniques to compare the efficiency of s-reps with boundary representations created using SPHARM-PDM. We observe that principal component analysis produces a more compact shape space using s-reps, needing fewer modes to represent 90% of the population variation, while distance-weighted discrimination shows that s-reps provide more significant classification results between valves with less regurgitation and those with more. These results demonstrate the power of using s-reps for relating structure and function of the tricuspid valve.