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In Vivo Biodistribution and Pharmacokinetics of Optimized Magnetic Particle Imaging Tracers

  • Amit P. Khandhar
  • R. Matthew Ferguson
  • Kannan M. Krishnan
Part of the Springer Proceedings in Physics book series (SPPHY, volume 140)

Abstract

Magnetic particle imaging (MPI) is an emerging magnetic nanoparticle detection technique that has great potential as a novel biomedical imaging procedure. Particularly, MPI offers a safer real-time option over conventional x-ray angiography procedures since it uses safe magnetic fields (no ionizing radiation) and biocompatible superparamagnetic magnetite (Fe3O4) nanoparticle tracers, which are the source of the signal and play a significant role in spatial resolution. Current tracer formulations such as Resovist® offer poor spatial resolution, and thus, inadequate performance for high-quality angiographies. Alternatively, our superparamagnetic magnetite (SuperMag) tracers show 30% improvement in spatial resolution compared to Resovist®. However, an ideal MPI tracer consists of a balance between an optimized magnetic core and a biocompatible shell that enhances circulation times combined with appropriate functionalization necessary to enhance the tracer’s bioavailability. For angiographies, tracer availability in the vasculature is of utmost importance to determine the most effective method of administration and ensure sufficient time for the imaging procedure. In this preliminary study we report pharmacokinetics and biodistribution characteristics of SuperMag tracers in an animal model. SuperMag tracers were formulated with variations in the polymeric shell and subsequently tested in CD-1 mice. Dose-dependent biodistribution was studied using MR-imaging and post-mortem histology analysis. Implications of in vivo circulation characteristics on MPI angiography procedures are discussed.

Keywords

Animal Model Significant Role Magnetic Material Sufficient Time Magnetic Particle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Amit P. Khandhar
    • 1
  • R. Matthew Ferguson
    • 2
  • Kannan M. Krishnan
    • 1
  1. 1.LodeSpin Labs LLCSeattleUSA
  2. 2.Materials Science & EngineeringUniversity of WashingtonSeattleUSA

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