Quantification of SPIO nanoparticles in vivo using the finite perturber method

Authors

  • Jason Langley,

    1. Department of Physics and Astronomy, BioImaging Research Center (BIRC), The University of Georgia, Athens, Georgia, USA
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  • Wei Liu,

    1. Phillips Research Laboratories, Briarcliff Manor, New York, USA
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  • E. Kay Jordan,

    1. Frank Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
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  • J. A. Frank,

    1. Frank Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
    2. Intramural Research Program, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland, USA
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  • Qun Zhao

    Corresponding author
    1. Department of Physics and Astronomy, BioImaging Research Center (BIRC), The University of Georgia, Athens, Georgia, USA
    • 201 Cedar Street, Department of Physics and Astronomy, University of Georgia, Athens, GA 30602===

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Abstract

The susceptibility gradients generated by super-paramagnetic iron oxide (SPIO) nanoparticles make them an ideal contrast agent in magnetic resonance imaging. Traditional quantification methods for SPIO nanoparticle-based contrast agents rely on either mapping Tinline image values within a region or by modeling the magnetic field inhomogeneities generated by the contrast agent. In this study, a new model-based SPIO quantification method is introduced. The proposed method models magnetic field inhomogeneities by approximating regions containing SPIOs as ensembles of magnetic dipoles, referred to as the finite perturber method. The proposed method was verified using data acquired from a phantom and in vivo mouse models. The phantom consisted of an agar solution with four embedded vials, each vial containing known but different concentrations of SPIO nanoparticles. Gaussian noise was also added to the phantom data to test performance of the proposed method. The in vivo dataset was acquired using five mice, each of which was subcutaneously implanted in the flanks with 1 × 105 labeled and 1 × 106 unlabeled C6 glioma cells. For the phantom data set, the proposed algorithm was generate accurate estimations of the concentration of SPIOs. For the in vivo dataset, the method was able to give estimations of the concentration within SPIO-labeled tumors that are reasonably close to the known concentration. Magn Reson Med, 2011. © 2010 Wiley-Liss, Inc.

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