Progress in magnetic particle imaging signal and iron quantification methods in vivo - application to long circulating SPIONs

Bulk Magnetic Properties Arise from Micron-Sized Supraparticle Interactions and Can be Modified on the Nanoscale

Magnetic supraparticles (SPs) can be employed as micron-sized particulate additives in arbitrary objects to serve as ID-tag or recorder of environmental triggers. Combined with magnetic particle spectroscopy (MPS), which enables read-out of the magnetic information in ambient conditions within seconds, magnetic SPs represent a powerful approach to equip materials with information. The encoded information relies on magnetic interactions within the SPs (intra-SP interactions) of chosen nanoparticles (NPs). However, possible magnetic interactions between SPs (inter-SP interactions), that might alter the MPS signal as well, have been neglected so far. Herein, it is elucidated that significant inter-SP interactions exist and that they can be tailored via adjustments in the SP structure, i.e., by defined adjustments of their intra-interaction as revealed by 3D-MuMax simulations and experiments in viscous fluids. Superparamagnetic iron oxide nanoparticle-based SP powders with strong inter-SP interactions exhibit significantly different MPS signals compared to their state after being incorporated into a matrix. Powders with weak inter-SP interactions (achieved by integration of non-magnetic SiO2 nanoparticles) show almost no signal change before and after incorporation. Both extremes of inter-SP interactions can be beneficial for various application scenarios and can be tailored on the nano-scale due to the interdependency of intra- and inter-SP interactions.

Machine Learning and Deep Learning Applications in Magnetic Particle Imaging

In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non-ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X-space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence-based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.