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Steady-State Tissue Oxygen Distributions Calculated by a Green’s Function Method and a Finite Difference Method: A Comparison*

机译:用格林函数法和有限差分法计算的稳态组织氧分布:比较*

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Simulations that are meant to determine the steady-state distribution of a diffusible solute such as oxygen in tissues have typically used finite difference methods to solve the diffusion equation. Finite difference methods require a tissue mesh with enough points to resolve oxygen gradients near and between discrete blood vessels. The large number of points that are typically required can make these calculations very slow. In this paper, we investigate a numerical method known as the Green’s function method which is not bound by the same constraint. The Green’s function method is expected to yield an accurate oxygen distribution more quickly by requiring fewer mesh points. Both methods were applied to calculate the steady state oxygen distribution in a model simulation region. When the Green’s function calculation used meshes with 1⁄2, 1⁄4 and, 1⁄8 of the resolution required for the finite-difference mesh, there was good agreement with the finite difference calculation in all cases. When the volume of the domain was increased 8-fold the Green’s function method was able to calculate the O2 field in 22 minutes, whereas the finite difference calculation is expected to take approximately 1 week. The number of steps required for the Green’s function calculation increases quadratically with the number of points in the tissue mesh. As a result, small meshes are calculated very quickly using Green’s functions, while for larger mesh sizes this method experiences a significant decrease in efficiency.
机译:旨在确定组织中诸如氧气的可扩散溶质的稳态分布的模拟通常使用有限差分法来求解扩散方程。有限差分方法需要具有足够点的组织网格来解析离散血管附近和之间的氧气梯度。通常需要大量的点,这会使这些计算非常缓慢。在本文中,我们研究了一种不受格林尼治函数法限制的数值方法。格林函数法预计将需要较少的网格点,从而更快地产生准确的氧气分布。两种方法都用于计算模型模拟区域中的稳态氧分布。当格林函数的计算使用有限差分网格所需分辨率的1⁄2、1⁄4和1⁄8的网格时,在所有情况下均与有限差分计算有很好的一致性。当域的体积增加8倍时,格林函数方法便可以计算出O 2 场在22分钟内完成,而有限差分计算预计将需要大约1周的时间。 Green函数的计算所需的步数随组织网格中点的数量成倍增加。结果,使用格林函数可以快速计算出较小的网格,而对于较大的网格,此方法的效率将大大降低。

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