Iron oxide magnetic nanoparticles based low-field MR thermometry

This paper reports on a highly accurate approach of magnetic resonance (MR) thermometry using iron oxide magnetic nanoparticles (MNPs) as temperature sensors. An empirical model for the description of the temperature dependent R ₂ relaxation rate is proposed by taking into account the temperature sensitivity of the MNP magnetization. The temperature sensitivity of the MNP magnetization (η) and the temperature sensitivity of the R ₂ relaxation rate (κ) are simulated with the proposed empirical models to investigate their dependence on the magnetic field and the particle size. Simulation results show the existence of optimal magnetic fields H_oη and H_oκ that maximize the temperature sensitivities η and κ. Furthermore, simulations and experiments demonstrate that the optimal magnetic field H_oη (H_oκ ) decreases with increasing the particle size. Experiments on temperature dependent R ₂ relaxation rate are performed at different magnetic fields for MNP samples with different iron concentrations. Experimental results show that the proposed MR thermometry using MNPs as temperature sensors allows a temperature estimation accuracy of about 0.05 °C. We believe that the achieved approach of highly accurate MR thermometry is of great interest and significance to biomedicine and biology.