利用曲率从电解剖作图数据中恢复本征传导速度和动作电位持续时间。

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Caroline Roney , Gernot Plank , Shohreh Honarbakhsh , Caterina Vidal Horrach , Simone Pezzuto , Edward Vigmond
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引用次数: 0

摘要

电解剖测绘系统测量激活和恢复在心脏表面的扩散。在心脏组织中的传播是复杂的组织结构,产生空间变化的各向异性电导率,导致复杂的波前。已知波前曲率会影响传导速度(CV)和动作电位持续时间(APD)。在这项研究中,我们试图更好地定义波前曲率对这些特性的影响,以及电导率的影响,以便恢复固有的组织特性。测量了几种离子模型的正、负曲率下CV和APD对曲率的依赖性,并在实际的二维和三维仿真中进行了验证。并对临床资料进行分析。结果表明,APD和CV的影响可以用简单的公式很好地描述,如果光纤的结构已知,则可以恢复本征传播速度。几何曲率严格由波前形状决定,忽略了纤维结构,导致大面积的伪高曲率。这对于确定慢传导的病理区是很重要的。在研究的模拟中,曲率对APD的调制最多为20毫秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recovering intrinsic conduction velocity and action potential duration from electroanatomic mapping data using curvature
Electroanatomic mapping systems measure the spread of activation and recovery over the surface of the heart. Propagation in cardiac tissue is complicated by the tissue architecture which produces a spatially varying anisotropic conductivity, leading to complex wavefronts. Curvature of the wavefront is known to affect both conduction velocity (CV) and action potential duration (APD). In this study, we sought to better define the impact of wavefront curvature on these properties, as well as the influence of conductivity, in order to recover intrinsic tissue properties. The dependence of CV and APD on curvature were measured for positive and negative curvatures for several ionic models, and then verified in realistic 2D and 3D simulations. Clinical data were also analysed. Results indicate that the effects of APD and CV are well described by simple formulae, and if the structure of the fibre is known, the intrinsic propagation velocities can be recovered. Geometrical curvature, as determined strictly by wavefront shape and ignoring the fibre structure, leads to large regions of spurious high curvature. This is important for determining pathological zones of slow conduction. In the simulations studied, curvature modulated APD by at most 20 ms.
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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