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Imaging Magnetic Nanoparticle Distributions by Atomic Magnetometry-Based Susceptometry

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We introduce a Magnetic Particle Imaging Susceptometer (MPIS) that uses a high-sensitivity atomic magnetometer (AM) for recording the spatial distribution of fluid-suspended magnetic nanoparticles. We have evaluated the MPIS performance… Click to show full abstract

We introduce a Magnetic Particle Imaging Susceptometer (MPIS) that uses a high-sensitivity atomic magnetometer (AM) for recording the spatial distribution of fluid-suspended magnetic nanoparticles. We have evaluated the MPIS performance by one-dimensional scans of structured nanoparticle phantoms, demonstrating, in particular, resolutions of ≈2.5 mm prior to deconvolution and ${\ll }1$ mm after deconvolution. Our instrument conceptually follows the general principle of Magnetic Particle Imaging (MPI) for encoding spatial distributions into magnetic flux density variations. Conversely to previously demonstrated MPI methods, MPIS works in time-space by recording time series of the sample’s magnetic response including all Fourier components. The device deploys a specifically designed system of coils, a low-frequency excitation scheme, and a simple source localization algorithm. The difference of the AM’s frequency response with respect to the conventional receive coil detection allows us to work at much lower driving frequencies. We demonstrate operation at frequencies on the order of 100 Hz, enabling the beneficial use of larger nanoparticles. The spatial distribution encoded into the particles’ susceptibility needs a much lower excitation field amplitude compared to conventional MPI scanners. These two features make MPIS least harmful for biological samples and subjects compared to conventional MPI scanners. We also address performance characteristics and other possible applications of MPIS.

Keywords: magnetic nanoparticle; imaging magnetic; nanoparticle distributions; distributions atomic; atomic magnetometry; magnetometry based

Journal Title: IEEE Transactions on Medical Imaging
Year Published: 2020

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