A Blood Flow Volume Linear Inversion Model Based on Electromagnetic Sensor for Predicting the Rate of Arterial...

Area of Science:

• Biomedical Engineering
• Medical Physics
• Computational Modeling

Background:

• Arterial stenosis poses a significant health risk, necessitating accurate and early diagnostic methods.
• Current diagnostic techniques may be invasive or lack precision in quantifying stenosis severity.
• Electromagnetic induction offers a potential non-invasive approach for hemodynamic assessment.

Purpose of the Study:

• To develop and validate a mathematical model for measuring blood flow using electromagnetic induction.
• To predict the rate of arterial stenosis based on blood flow measurements.
• To assess the model's accuracy and clinical significance for early diagnosis.

Main Methods:

• Utilized an electrode sensor to capture induced potential differences on the skin surface within a uniform magnetic field.
• Constructed an inversion matrix employing weight function theory and the finite element method.
• Developed a blood flow volume inversion model integrating induction potentials and the inversion matrix.
• Employed COMSOL software for simulations using a 3D geometric model of the ulnar artery with varying stenosis rates.

Main Results:

• The developed inversion model demonstrated high accuracy in blood flow measurement.
• The model accurately predicted the rate of arterial stenosis.
• Simulation results confirmed the model's efficacy in a realistic arterial geometry.

Conclusions:

• The electromagnetic induction-based mathematical model provides a highly accurate method for blood flow measurement and arterial stenosis prediction.
• This non-invasive technique is significant for the early diagnosis of arterial stenosis and other vascular diseases.
• The model offers a promising tool for improving patient outcomes through timely intervention.