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Improving Model-Based MPI Image Reconstructions: Baseline Recovery, Receive Coil Sensitivity, Relaxation and Uncertainty Estimation

Published

Author(s)

Mark-Alexander Henn, Klaus Natorf Quelhas, Thinh Bui, Solomon I. Woods

Abstract

Image reconstruction is an integral part ofMagnetic Particle Imaging (MPI). Over the last years, several methods have been proposed for reconstructing MPI images more efficiently and accurately. One major challenge for model-based MPI image reconstruction methods is the realistic modeling of the measurement system; effects like non-linear gradient fields, non-uniformdrive fields, space-dependent coil sensitivities, base frequency filtering and particle relaxation, if not accounted for in the model, may yield inaccurate reconstructions. This works addresses these issues by means of an image reconstruction method that accounts for the coil sensitivity, baseline recovery and particle relaxation. We investigate the proposed approach for a 1D MPI setup, and provide a first approach for the calculation of the uncertainties of the reconstructed images.
Citation
International Journal on Magnetic Particle Imaging
Volume
8
Issue
1

Keywords

Magnetic Particle Imaging, Inverse Problems, Uncertainty Analysis

Citation

Henn, M. , Natorf Quelhas, K. , Bui, T. and Woods, S. (2022), Improving Model-Based MPI Image Reconstructions: Baseline Recovery, Receive Coil Sensitivity, Relaxation and Uncertainty Estimation, International Journal on Magnetic Particle Imaging, [online], https://doi.org/10.18416/IJMPI.2022.2208001, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933567 (Accessed October 1, 2022)
Created August 5, 2022