International Journal on Magnetic Particle Imaging IJMPI
Vol. 8 No. 1 Suppl 1 (2022): Int J Mag Part Imag
https://doi.org/10.18416/IJMPI.2022.2203052

Proceedings Articles

Computational modeling of superferromagnetism in finite-length chains of superparamagnetic Iron Oxide tracers for use in super-resolution Magnetic Particle Imaging

Main Article Content

Chinmoy Saayujya (University of California, Berkeley), K. L. Barry Fung (University of California, Berkeley), Quincy Huynh (University of California, Berkeley), Caylin Colson (University of California, Berkeley), Benjamin Fellows (University of California, Berkeley), Prashant Chandrasekharan (University of California, Berkeley), Steven M. Conolly (University of California, Berkeley)

Copyright (c) 2022 Chinmoy Saayujya, K. L. Barry Fung, Quincy Huynh, Caylin Colson, Benjamin Fellows, Prashant Chandrasekharan, Steven M. Conolly

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract




Magnetic Particle Imaging (MPI) is a novel tracer imaging modality that images the spatial distribution of super- paramagnetic iron oxide nanoparticles (SPIOs), allowing for the sensitive and radiation-free imaging of labeled cells and targeted disease. Recent works have shown that at high concentrations, SPIOs display extremely sharp magnetic responses, resulting in 10-fold resolution and signal improvements. Dubbed superferromagnetic iron oxide particles (SFMIOs), these particles appear to interact with neighbours, effectively amplifying applied fields. This work performs a simulation of ensembles of linear chains of interacting SPIOs to elucidate SFMIO behavior and guide practical constraints in SFMIO synthesis. We show that working within certain physical constraints (chain length distributions and SPIO separation) preserves the improvements observed from SFMIOs.




Article Details

Most read articles by the same author(s)

<< < 1 2