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Advanced Plaque Modeling for Atherosclerosis Detection Using Molecular Communication

Alexander Wietfeld, Pit Hofmann, Jonas Fuchtmann, Pengjie Zhou, Ruifeng Zheng, Juan A. Cabrera, Frank H. P. Fitzek, Wolfgang Kellerer

Abstract

As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts. Previously, molecular communication (MC) models have been proposed to capture and analyze the natural processes inside the human body and to support the development of diagnosis and treatment methods. In the future, synthetic MC networks are envisioned to span the human body as part of the Internet of Bio-Nano Things (IoBNT), turning blood vessels into physical communication channels. By observing and characterizing changes in these channels, MC networks could play an active role in detecting diseases like atherosclerosis. In this paper, building on previous preliminary work for simulating an MC scenario in a plaque-obstructed blood vessel, we evaluate different analytical models for non-Newtonian flow and derive associated channel impulse responses (CIRs). Additionally, we add the crucial factor of flow pulsatility to our simulation model and investigate the effect of the systole-diastole cycle on the received particles across the plaque channel. We observe a significant influence of the plaque on the channel in terms of the flow profile and CIR across different emission times in the cycle. These metrics could act as crucial indicators for early non-invasive plaque detection in advanced future MC methods.

Advanced Plaque Modeling for Atherosclerosis Detection Using Molecular Communication

Abstract

As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts. Previously, molecular communication (MC) models have been proposed to capture and analyze the natural processes inside the human body and to support the development of diagnosis and treatment methods. In the future, synthetic MC networks are envisioned to span the human body as part of the Internet of Bio-Nano Things (IoBNT), turning blood vessels into physical communication channels. By observing and characterizing changes in these channels, MC networks could play an active role in detecting diseases like atherosclerosis. In this paper, building on previous preliminary work for simulating an MC scenario in a plaque-obstructed blood vessel, we evaluate different analytical models for non-Newtonian flow and derive associated channel impulse responses (CIRs). Additionally, we add the crucial factor of flow pulsatility to our simulation model and investigate the effect of the systole-diastole cycle on the received particles across the plaque channel. We observe a significant influence of the plaque on the channel in terms of the flow profile and CIR across different emission times in the cycle. These metrics could act as crucial indicators for early non-invasive plaque detection in advanced future MC methods.

Paper Structure

This paper contains 13 sections, 16 equations, 6 figures.

Table of Contents

  1. Introduction
  2. MC Model Setup
  3. Analytical Approaches
  4. Analytical Flow Profiles and Impulse Responses
  5. Newtonian Fluid
  6. Power-Law Fluid
  7. Herschel-Bulkley Model
  8. Venturi Effect
  9. Model Comparison and Flow Profile Simulation
  10. Pulsatile Inlet Velocity Simulation Approach
  11. Simulation Control
  12. Simulation Results
  13. Conclusion and Outlook

Figures (6)

  • Figure 1: Schematic of the considered atherosclerosis scenario; adapted from Hofmann2024.
  • Figure 2: Human carotid artery pulsatile flow profile Gay2008Holdsworth1999.
  • Figure 3: Relative reduction in traversal time due to the Venturi effect in the narrower channel with plaque of size $r_\mathrm{p}$.
  • Figure 4: Comparison of three analytical fluid flow models, two incorporating non-Newtonian effects. Left: flow velocity profiles; Right: CIR as calculated in Eqs. (\ref{['eq:cir_newtonian']}), (\ref{['eq:cir_pl']}), and (\ref{['eq:cir_hb']}).
  • Figure 5: Comparison of different flow velocity profiles obtained from the OpenFOAM simulation at three points in the channel (in front of, at, and behind the plaque). The two non-Newtonian analytical models have been fitted to the case for $x=0.01m$.
  • ...and 1 more figures