Mechanical and Aerospace Engineering Department, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
J R Soc Interface. 2022 Aug;19(193):20220306. doi: 10.1098/rsif.2022.0306. Epub 2022 Aug 10.
Capillary blood vessels in the body partake in the exchange of gas and nutrients with tissues. They are interconnected via multiple vascular junctions forming the microvascular network. Distributions of blood flow and red cells (RBCs) in such networks are spatially uneven and vary in time. Since they dictate the pathophysiology of tissues, their knowledge is important. Theoretical models used to obtain flow and RBC distribution in large networks have limitations as they treat each vessel as a one-dimensional segment and do not explicitly consider cell-cell and cell-vessel interactions. High-fidelity computational models that accurately model each individual RBC are computationally too expensive to predict haemodynamics in large vascular networks and over a long time. Here we investigate the applicability of machine learning (ML) techniques to predict blood flow and RBC distributions in physiologically realistic vascular networks. We acquire data from high-fidelity simulations of deformable RBC suspension flowing in the networks. With the flow and haematocrit specified at an inlet of vasculature, the ML models predict the time-averaged flow rate and RBC distributions in the entire network, time-dependent flow rate and haematocrit in each vessel and vascular bifurcation in isolation over a long time, and finally, simultaneous spatially and temporally evolving quantities through the vessel hierarchy in the networks.
体内的毛细血管参与与组织之间的气体和营养物质交换。它们通过多个血管连接相互连接，形成微血管网络。这种网络中血流和红细胞（RBC）的分布在空间上不均匀，并且随时间变化。由于它们决定了组织的病理生理学，因此了解它们很重要。用于获得大型网络中流动和 RBC 分布的理论模型存在局限性，因为它们将每个血管视为一维段，并且没有明确考虑细胞-细胞和细胞-血管相互作用。准确模拟每个单独 RBC 的高保真计算模型在计算上过于昂贵，无法预测大型血管网络和长时间内的血液动力学。在这里，我们研究了机器学习（ML）技术在预测生理现实血管网络中的血流和 RBC 分布中的适用性。我们从网络中流动的可变形 RBC 悬浮液的高保真模拟中获取数据。在血管入口处指定流动和血细胞比容，ML 模型预测整个网络中的时均流速和 RBC 分布、每个血管和血管分支的随时间变化的流速和血细胞比容、以及最终通过网络中的血管层次结构同时进行空间和时间演变的数量。
