Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:5425-5428. doi: 10.1109/EMBC46164.2021.9630632.
State-of-the-art solvers for in silico cardiac electro-physiology employ the Finite Element Method to solve complex anatomical models. While this is a robust and accurate tech-nique, it requires a high-quality mesh to prevent its accuracy from being severely deteriorated. The generation of a good quality mesh for realistic anatomical models can be very time-consuming, making the translation to the clinics challenging, especially if we try to use patient-specific geometries.Aiming to tackle this challenge, we propose an image-based model generation approach based on the meshfree Mixed Col-location Method. The flexibility provided by this method during model generation allows building meshfree models directly from the image data in an automatic procedure. Furthermore, this approach allows interpreting the simulation results directly in the voxel coordinates system of the image.We simulate electrical propagation in a porcine biventricular model with the proposed method and we compare the results with those obtained using the Finite Element Method. We conclude that the proposed method can generate results that are in good agreement with the Finite Element Method solution, alleviating the requirement of a mesh and user-input during modeling with only minimum efficiency overhead.
用于计算机心脏电生理学的最先进求解器采用有限元方法来解决复杂的解剖模型。虽然这是一种强大而准确的技术，但它需要高质量的网格来防止其准确性严重恶化。为现实解剖模型生成高质量的网格可能非常耗时，这使得向临床的转化具有挑战性，特别是如果我们尝试使用患者特定的几何形状。为了应对这一挑战，我们提出了一种基于无网格混合配置方法的基于图像的模型生成方法。在模型生成过程中，该方法提供的灵活性允许直接从图像数据以自动的方式构建无网格模型。此外，这种方法允许直接在图像的体素坐标系中解释模拟结果。我们使用提出的方法模拟了猪双心室模型中的电传播，并将结果与使用有限元方法获得的结果进行了比较。我们得出结论，提出的方法可以生成与有限元方法解决方案非常吻合的结果，减轻了在建模过程中对网格和用户输入的要求，同时仅带来最小的效率开销。
