Relaxation spectral analysis in multi-contrast vascular magnetic particle imaging

Area of Science:

• Biomedical Engineering
• Materials Science
• Medical Imaging

Background:

• Magnetic nanoparticles (MNPs) are crucial non-ionizing tracers for vascular, molecular, and neuroimaging.
• Understanding Néel (internal) and Brownian (external) relaxation mechanisms is key to MNP characterization.
• Accurate measurement of relaxation times aids in predicting MNP types and hydrodynamic states.
• Conventional sinusoidal excitation in MPI struggles to differentiate Néel and Brownian relaxation components.

Purpose of the Study:

• To develop a multi-exponential relaxation spectral analysis method for independently measuring Néel and Brownian relaxation times.
• To apply this method to magnetization recovery in pulsed vascular Magnetic Particle Imaging (MPI).

Main Methods:

• Utilized pulsed excitation in a trapezoidal-waveform relaxometer with Synomag-D samples of varying viscosities.
• Employed inverse Laplace transform-based spectral analysis (PDCO method) on relaxation decay signals.
• Investigated samples with different glycerol and gelatin concentrations to decouple relaxation times.
• Simulated spectral imaging of a digital vascular phantom with viscous and immobilized MNPs.

Main Results:

• Identified distinct Néel and Brownian relaxation time peaks in samples with varying viscosities.
• Observed a positive linear correlation between Brownian relaxation time and viscosity (0.9–3.2 mPa·s), with saturation beyond 3.2 mPa·s.
• Found that Néel relaxation time slightly decreased with viscosity, also showing saturation.
• Demonstrated differentiation of plaque, catheter, and vessel regions in simulated Brownian relaxation time maps.
• Determined optimal field amplitude for Brownian relaxation time sensitivity around 4.5 mT.

Conclusions:

• Successfully quantified Néel and Brownian relaxation times using spectral analysis with pulsed excitation.
• The developed method enables multi-contrast imaging in vascular MPI.
• This quantitative assessment holds significant potential for advanced MNP-based imaging techniques.