Field-free Line Magnetic Particle Imaging: Radon-based Artifact Reduction with Motion Models

Abstract Magnetic particle imaging is a promising medical imaging technique. Applying changing magnetic fields to tracer material injected into the object under investigation results in a change in magnetization. Measurement of related induced voltage signals enables reconstruction of the particle distribution. For the field-free line scanner the scanning geometry is similar to the one in computerized tomography. We make use of these similarities to derive a forward model for dynamic particle concentrations. We validate our theoretical findings for synthetic data. By utilizing information about the object’s dynamics in terms of a diffeomorphic motion model, we are able to jointly reconstruct the particle concentration and the corresponding dynamic Radon data without or reduced motion artifacts. Thereby, we apply total variation regularization for the concentration and an optional sparsity constraint on the Radon data.

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Publication Details

Journal
Sensing and Imaging
Published
2024-12-03
DOI
https://doi.org/10.1007/s11220-024-00524-y
Primary Topic
Characterization and Applications of Magnetic Nanoparticles
Type
article
Field-Weighted Citation Impact
0.00

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article

Field-free Line Magnetic Particle Imaging: Radon-based Artifact Reduction with Motion Models

Stephanie Blanke, Christina Brandt
Sensing and Imaging
Characterization and Applications of Magnetic Nanoparticles
article

Field-free Line Magnetic Particle Imaging: Radon-based Artifact Reduction with Motion Models

Stephanie Blanke, Christina Brandt
article en

Abstract

Abstract Magnetic particle imaging is a promising medical imaging technique. Applying changing magnetic fields to tracer material injected into the object under investigation results in a change in magnetization. Measurement of related induced voltage signals enables reconstruction of the particle distribution. For the field-free line scanner the scanning geometry is similar to the one in computerized tomography. We make use of these similarities to derive a forward model for dynamic particle concentrations. We validate our theoretical findings for synthetic data. By utilizing information about the object’s dynamics in terms of a diffeomorphic motion model, we are able to jointly reconstruct the particle concentration and the corresponding dynamic Radon data without or reduced motion artifacts. Thereby, we apply total variation regularization for the concentration and an optional sparsity constraint on the Radon data.

Sensing and ImagingVol. 26(1)
Universität Hamburg (DE), Universitätsmedizin Greifswald (DE), Universität Greifswald (DE)
Deutsche Forschungsgemeinschaft, Universität Hamburg
Openalex Percentile: Top 77%
Characterization and Applications of Magnetic Nanoparticles
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