IEEE Trans Biomed Eng. 2018 Dec;65(12):2760-2768. doi: 10.1109/TBME.2018.2815504. Epub 2018 Mar 12.
This work presents a new algorithm for the construction of a model for the Purkinje network (PN) of the heart.
The algorithm is based on a method called constructive constrained optimization (CCO), which was reformulated for the specific case of automatic PN generation. The proposed optimization-based algorithm is referred to as constructive optimization (CO). The CO method iteratively constructs the PN by minimizing the total length of the generated PN tree. In addition, it can take into account some important topological information of the PN, such as the location of the Purkinje-muscle junctions and the average bifurcation angle found in the literature.
To validate the model, the new method was compared with the classical L-system method for generating PN models and to a recently proposed image-based technique.
The results show that the CO is able to construct PNs with geometric features and activation times that are in good agreement with those reported in the literature and to those obtained by the other aforementioned alternatives.
本研究提出了一种新的算法，用于构建心脏浦肯野网络（PN）模型。
该算法基于一种称为构造约束优化（CCO）的方法，针对自动生成 PN 的特定情况对其进行了重新表述。所提出的基于优化的算法称为构造优化（CO）。CO 方法通过最小化生成 PN 树的总长度来迭代地构建 PN。此外，它还可以考虑 PN 的一些重要拓扑信息，如文献中发现的浦肯野-肌肉连接处的位置和平均分叉角。
为了验证模型，将新方法与经典的 L 系统方法生成 PN 模型进行了比较，并与最近提出的基于图像的技术进行了比较。
结果表明，CO 能够构建出具有几何特征和激活时间的 PN，与文献中报道的以及上述其他替代方法获得的结果非常吻合。
