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

arXiv:2510.11743v1 Announce Type: new
Abstract: 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.