Dynamic cerebral blood flow assessment based on electromagnetic coupling sensing and image feature analysis

Area of Science:

• Biomedical Engineering
• Neuroscience
• Medical Imaging

Background:

• Dynamic cerebral blood flow (CBF) assessment is vital for stroke management and prognosis.
• Current clinical methods for dynamic CBF evaluation lack safety, reliability, and effectiveness.

Purpose of the Study:

• To develop and validate a novel CBF monitoring system using electromagnetic coupling sensing (ECS).
• To assess the system's capability in detecting immediate responses to altered intracranial blood supply and classifying overall CBF levels.

Main Methods:

• Developed an ECS-based system to detect variations in brain conductivity and dielectric constant via resonant frequency (RF).
• Evaluated system performance using a physical model of pulsatile blood flow and tested in 29 healthy volunteers.
• Monitored cerebral oxygen (CO), cerebral blood flow velocity (CBFV), and RF data before and after caffeine consumption.
• Analyzed RF and CBFV trends during induced changes in vascular stiffness and compared with CO data.
• Employed image feature analysis and machine learning algorithms for dynamic CBF level assessment.

Main Results:

• The ECS system demonstrated a 3-4 times enhanced detection range and depth compared to conventional electromagnetic techniques.
• The system effectively captured CBF responses to varying intravascular pressures and vascular stiffness.
• In volunteers, caffeine intake led to decreased CO and diminished RF pulsation amplitude, correlating with increased vascular stiffness.
• Machine learning algorithms accurately classified overall CBF levels based on extracted image features.

Conclusions:

• The proposed ECS and image feature analysis methodology enables real-time monitoring of intracranial blood supply changes.
• This system provides a safe, reliable, and effective tool for dynamic CBF assessment in various physiological conditions.
• The technology holds promise for improved personalized management and treatment strategies for conditions like stroke.