标题： 血管网络中的血流动力学传输混合逆高斯分布（MIGHT） 显示英文标题

标题： Mixture of Inverse Gaussians for Hemodynamic Transport (MIGHT) in Vascular Networks

摘要： 合成分子通信（MC）在心血管系统（CVS）中是许多设想的人体医疗应用的关键使能技术，例如靶向药物输送、早期癌症检测和持续健康监测。 此类应用的MC系统设计需要适用于信号分子通过复杂血管网络（VNs）传播的合适模型。 现有的理论模型分析可解性有限，并且缺乏闭式解，使得大规模VNs的分析要么不可行，要么没有洞察力。 为了克服这些限制，本文提出了一种新的闭式物理模型，称为MIGHT，用于信号分子通过复杂VNs的对流-扩散传输。 该模型将接收到的分子通量表示为逆高斯（IG）分布的加权和，其参数由网络的物理特性确定。 所提出的模型通过与现有卷积模型和有限元仿真进行比较进行了验证。 此外，我们表明该模型可用于将大规模VNs简化为保留基本传输动态的简化表示，并用于根据来自未知VNs的接收信号估计代表性的VN。 显示英文摘要

摘要： Synthetic molecular communication (MC) in the cardiovascular system (CVS) is a key enabler for many envisioned medical applications in the human body, such as targeted drug delivery, early cancer detection, and continuous health monitoring. The design of MC systems for such applications requires suitable models for the signaling molecule propagation through complex vessel networks (VNs). Existing theoretical models offer limited analytical tractability and lack closed-form solutions, making the analysis of large-scale VNs either infeasible or not insightful. To overcome these limitations, in this paper, we propose a novel closed-form physical model, termed MIGHT, for advection-diffusion-driven transport of signaling molecules through complex VNs. The model represents the received molecule flux as a weighted sum of inverse Gaussian (IG) distributions, parameterized by physical properties of the network. The proposed model is validated by comparison with an existing convolution-based model and finite-element simulations. Further, we show that the model can be applied for the reduction of large VNs to simplified representations preserving the essential transport dynamics and for estimating representative VN based on received signals from unknown VNs.