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Improving Blood Vessel Tortuosity Measurements via Highly Sampled Numerical Integration of the Frenet-Serret Equations

机译:通过高度采样的FRENET-SERRET方程的高度采样数值集成改善血管曲折测量

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摘要

Measures of vascular tortuosity-how curved and twisted a vessel is-are associated with a variety of vascular diseases. Consequently, measurements of vessel tortuosity that are accurate and comparable across modality, resolution, and size are greatly needed. Yet in practice, precise and consistent measurements are problematic-mismeasurements, inability to calculate, or contradictory and inconsistent measurements occur within and across studies. Here, we present a new method of measuring vessel tortuosity that ensures improved accuracy. Our method relies on numerical integration of the Frenet-Serret equations. By reconstructing the three-dimensional vessel coordinates from tortuosity measurements, we explain how to identify and use a minimally-sufficient sampling rate based on vessel radius while avoiding errors associated with oversampling and overfitting. Our work identifies a key failing in current practices of filtering asymptotic measurements and highlights inconsistencies and redundancies between existing tortuosity metrics. We demonstrate our method by applying it to manually constructed vessel phantoms with known measures of tortuousity, and 9,000 vessels from medical image data spanning human cerebral, coronary, and pulmonary vascular trees, and the carotid, abdominal, renal, and iliac arteries.
机译:血管腐烂的措施 - 如何弯曲和扭曲血管 - 与各种血管疾病有关。因此,大大需要测量血管曲折,这些灰度曲折,横跨模态,分辨率和尺寸可比。然而,实际上,精确和一致的测量是有问题的 - 不可能的,无法计算,或者在研究中发生矛盾的测量和矛盾的测量。在这里,我们介绍了一种测量血管曲折的新方法,可确保提高精度。我们的方法依赖于FRENET-SERRET方程的数值集成。通过重建曲折测量的三维血管坐标,我们解释了如何基于血管半径来识别和使用最小足够的采样率,同时避免与过采样和过度装备相关的误差。我们的工作确定了当前过滤渐近测量的实践的关键,并突出了现有曲折度量之间的不一致性和冗余。我们通过将其应用于手动构建血管幽灵,通过已知的曲折措施和来自跨越人类脑,冠状动脉和肺血管树的医学图像数据的9,000艘船以及颈动脉,腹部,肾和髂动脉进行9,000艘船。

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