Magnetic particle imaging: Model and reconstruction

Schomberg, H.

doi:10.1109/isbi.2010.5490155

Cited by 17 publications

(19 citation statements)

References 8 publications

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“…That is to say, without taking the natural log, CT is not strictly an LSI system. Existing works [12], [17] aim to analyze the MPI process in mathematical terms, but they do not endeavour to prove linearity or shift invariance. In this paper, we believe we have proved that MPI is indeed a LSI system with our three hypotheses, and that our experimental results show that the recovery of the first harmonic enables experimental MPI systems to be accurately modeled as LSI.…”

Section: Discussionmentioning

confidence: 99%

“…However, we were having difficulty reconstructing the multiple images into a composite image so that the reconstructed image is linear with the nanoparticle density irrespective of the shape of the phantom. A second approach to understanding MPI without a system matrix is seen in Schomberg [17], where the author also approaches the MPI process using an adiabatic assumption. The author’s theoretical approach is general and finds that the MPI signal is closely related to a convolution operator and has parallel goals to the approach presented here.…”

Section: Introductionmentioning

confidence: 99%

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Multidimensional X-Space Magnetic Particle Imaging

Goodwill

Conolly

2011

IEEE Trans. Med. Imaging

248

305

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Magnetic particle imaging (MPI) is a promising new medical imaging tracer modality with potential applications in human angiography, cancer imaging, in vivo cell tracking, and inflammation imaging. Here we demonstrate both theoretically and experimentally that multidimensional MPI is a linear shift-in-variant imaging system with an analytic point spread function. We also introduce a fast image reconstruction method that obtains the intrinsic MPI image with high signal-to-noise ratio via a simple gridding operation in x-space. We also demonstrate a method to reconstruct large field-of-view (FOV) images using partial FOV scanning, despite the loss of first harmonic image information due to direct feedthrough contamination. We conclude with the first experimental test of multidimensional x-space MPI.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multidimensional X-Space Magnetic Particle Imaging

Goodwill

Conolly

2011

IEEE Trans. Med. Imaging

248

305

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“…Signal Encoding. Schomberg [31] points out that by solving the right-hand side of (2.7) for I(t), I(t) = −P(r(t)) −1 H S (r(t)), the applied field H can be described in terms of the FFP rather than the current:…”

Section: The Mpi Signal Generation In Higher Dimensionsmentioning

confidence: 99%

“…The factor g denotes the nominal gradient of the static field. Finally, the linearity of H S implies that the applied field is of the form [31,10] H(x, t) = G(x − r(t)), and also that I(t) = −P −1 Gr(t).…”

Section: The Mpi Signal Generation In Higher Dimensionsmentioning

confidence: 99%

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Model-based reconstruction for magnetic particle imaging in 2D and 3D

März¹,

Weinmann²

2016

IPI

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We contribute to the mathematical modeling and analysis of magnetic particle imaging which is a promising new in-vivo imaging modality. Concerning modeling, we develop a structured decomposition of the imaging process and extract its core part which we reveal to be common to all previous contributions in this context. The central contribution of this paper is the development of reconstruction formulae for MPI in 2D and 3D. Until now, in the multivariate setup, only time consuming measurement approaches are available, whereas reconstruction formulae are only available in 1D. The 2D and the 3D (describing the real world) reconstruction formulae which we derive here are significantly different from the 1D situation -in particular there is no Dirac property in dimensions greater than one when the particle sizes approach zero. As a further result of our analysis, we conclude that the reconstruction problem in MPI is severely ill-posed. Finally, we obtain a model-based reconstruction algorithm.

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Magnetic Particle Imaging: From Tracer Design to Biomedical Applications in Vasculature Abnormality

Xie,

Zhai,

Zhou

et al. 2023

Advanced Materials

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Magnetic particle imaging (MPI) is an emerging non‐invasive tomographic technique based on the response of magnetic nanoparticles (MNPs) to oscillating drive fields at the center of a static magnetic gradient. In contrast with magnetic resonance imaging (MRI), which is driven by uniform magnetic fields and projects the anatomic information of the subjects, MPI directly tracks and quantifies MNPs in vivo without background signals. Moreover, it does not require radioactive tracers and has no limitations on imaging depth. This review first introduces the basic principles of MPI and important features of MNPs for advanced imaging sensitivity, spatial resolution, and targeted biodistribution. The latest research on optimized MPI tracers is reviewed based on their material composition, physical properties, and surface modifications. Since all the imaging benefits have led to a series of promising biomedical applications, we also discuss recent relational practices of MPI in vascular abnormalities, including cardiovascular and cerebrovascular systems, and cancers. Finally, some considerations of obstructions and recent progress closely related to the clinical translation of MPI are summarized to provide possible direction for future research and development.This article is protected by copyright. All rights reserved

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Magnetic particle imaging: Model and reconstruction

Cited by 17 publications

References 8 publications

Multidimensional X-Space Magnetic Particle Imaging

Multidimensional X-Space Magnetic Particle Imaging

Model-based reconstruction for magnetic particle imaging in 2D and 3D

Magnetic Particle Imaging: From Tracer Design to Biomedical Applications in Vasculature Abnormality

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