Digital representation of tissues in high compute bioelectromagnetics
Digital representation of tissues in high compute bioelectromagnetics
复制标题
高计算生物电磁学中组织的数字表示
DOI：
10.1016/j.cam.2021.113643
复制
发表时间：
2021-12-01
影响因子：
2.600
通讯作者：
Izabella Antoniuk
中科院分区：
文献类型：
--
作者：
Artur Krupa;Izabella Antoniuk
以数字形式表示任何类型的活组织是一个复杂而苛刻的问题，因为它的异构结构以及在这种过渡期间可能遇到的不同问题。通常，为了确保更快的计算时间，模型被简化，即通过平均可用参数。这样的方法可能导致省略基本特征，并且因此导致所获得的结果的较低准确度。在模拟中，生物电磁学使用不同的方法进行数值计算。此外，模型用于表示它们所描述的现象。本文提出了一种生物电磁仿真和研究领域中的组织表示方法，这是作者近年来在该领域开展工作的结果。该模型的描述被广泛讨论的文件，考虑到数值的不确定性，可靠性，平均或采用的几何形状的问题。每个概念都在示例中给出，沿着的是对模拟结果影响的可能最小化水平。这项工作还包括一个示例性的模型与组织的参数化描述和这些问题对实际结果的影响。我们提出了一个分析，显示哪些参数是必不可少的组织建模，模型的复杂性如何影响模拟，以及如何使用不同的组织模型可以影响总的模拟时间和输出效率之间的关系。仿真过程基于大规模云计算环境，提供了设计、仿真和优化解决方案，是众多可用解决方案之一。目前，本文中所描述的方法并没有标准地纳入广泛使用的求解器或模拟。所提出的结果可以导致当前组织建模方法的统一和标准化，提高电磁场对生物体影响研究的整体计算标准。
Representing any type of living tissue in digital form is a complex and demanding problem due to its heterogeneous structure as well as different issues that can be encountered during such transition. Usually, to ensure faster computation times, models are simplified, i.e. by averaging available parameters. Such an approach can result in omitting essential features and, consequently, lead to lower accuracy of obtained results. In simulations, bioelectromagnetism uses a different approach to numerical calculations. Additionally, models are used to represent the phenomena they describe. This article presents an approach to tissue representation in the field of bioelectromagnetic simulations and research, which is the result of work carried out by the authors in this field in recent years. The description of the model is widely discussed in the paper, taking into account the problem of numerical uncertainty, reliability, averaging or the adopted geometry. Each concept is presented in the examples, along with the possible level of minimization of the impact on the simulation results. The work also includes an exemplary model with a parametric description of tissues and the impact of these problems on the actual results. We present an analysis showing which parameters are essential for tissue modelling, how the complexity of a model influences a simulation and how using different tissue models can impact the relation between total simulation time and output effectiveness. The simulation process was based on a large-scale cloud computing environment with the presented design, simulation and optimization solution, one of the many available. Currently, methods described in this paper are not standardly incorporated in widely used solvers or simulations. Results presented can lead to unification and standardization of current tissue modelling methodologies, improving overall computation standards in research on the impact of electromagnetic field on living organisms.