Efficient Approach to Rank Performance of Magnetic Colloids for Magnetic Particle Imaging and Magnetic Particle Hyperthermia

Abstract

Magnetic particle imaging (MPI) is an emerging modality that can address longstanding technological challenges encountered with magnetic particle hyperthermia (MPH) cancer therapy. MPI is a tracer technology compatible with MPH for which magnetic nanoparticles (MNPs) provide signal for MPI and heat for MPH. Identifying whether a specific MNP formulation is suitable for both modalities is essential for clinical implementation. Current models predict that functional requirements of each modality impose conflicting demands on nanoparticle magnetic properties. This objective here is to develop a measurement and ranking scheme based on end-use performance to streamline evaluation of candidate MNP formulations. The measured MPI point-spread function (PSF) and specific loss power (SLP) is combined to generate a single numerical value for comparison on a relative ranking scale, or figure of merit (FoM). 12 aqueous iron-containing formulations are evaluated, including FDA-approved (parenteral) iron-containing colloids. MNPs with high (Synomag-D70: 123.4), medium (Synomag-D50: 63.2), and low (NanoXact: 0.147) FoM values are selected for in vivo validation of the selection scheme in subcutaneous 4T1 tumors. Results demonstrate that the proposed ranking accurately assessed the relative performance of MNPs for MPI and MPH. Data demonstrated that image quality and tumor temperature rise increased with FoM ranking, validating predictions. It isshown that the MPI signal correlated with MNP concentration in tissue. Computational heat transfer models anchored on tumor MPI data harmonized with experimental results to within an average of 2 °C when MNP content estimated from MPI data is included. Computational studies emphasized the importance of post-injection MNP quantitation and MPI spatial resolution.