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L1 data fitting for robust reconstruction in magnetic particle imaging: Quantitative evaluation on open mpi dataset

Kluth, T; Jin, B; (2020) L1 data fitting for robust reconstruction in magnetic particle imaging: Quantitative evaluation on open mpi dataset. International Journal on Magnetic Particle Imaging , 6 (2) pp. 1-23. 10.18416/ijmpi.2020.2012001. Green open access

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Abstract

Magnetic particle imaging is an emerging quantitative imaging modality, exploiting the unique nonlinear magneti-zation phenomenon of superparamagnetic iron oxide nanoparticles for recovering the concentration. Traditionally the reconstruction is formulated into a penalized least-squares problem with nonnegativity constraint, and then solved using a variant of Kaczmarz method which is often stopped early after a small number of iterations. Besides the phantom signal, measurements additionally include a background signal and a noise signal. In order to obtain good reconstructions, a preprocessing step of frequency selection to remove the deleterious influences of the noise is often adopted. In this work, we propose a complementary pure variational approach to noise treatment, by viewing highly noisy measurements as outliers, and employing the l1 data fitting, one popular approach from robust statistics. When compared with the standard approach, the resulting optimization problems can be solved by standard stand-alone optimizers, e.g., L-BFGS-B. Experiments with a public domain dataset, i.e., Open MPI dataset [1], show that it can give accurate reconstructions, and is less prone to noisy measurements, which is illus-trated by quantitative (PSNR / SSIM) and qualitative comparisons with the Kaczmarz method. We also investigate the performance of the Kaczmarz method for small iteration numbers quantitatively.

Type: Article
Title: L1 data fitting for robust reconstruction in magnetic particle imaging: Quantitative evaluation on open mpi dataset
Open access status: An open access version is available from UCL Discovery
DOI: 10.18416/ijmpi.2020.2012001
Publisher version: https://doi.org/10.18416/ijmpi.2020.2012001
Language: English
Additional information: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10124291
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