Jin, B;
Kluth, T;
(2019)
Enhanced reconstruction in magnetic particle imaging by whitening and randomized SVD approximation.
Physics in Medicine and Biology
, 64
, Article 125026. 10.1088/1361-6560/ab1a4f.
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Abstract
Magnetic particle imaging (MPI) is a medical imaging modality of recent origin, and it exploits the nonlinear magnetization phenomenon to recover a spatially dependent concentration of nanoparticles. In practice, image reconstruction in MPI is frequently carried out by standard Tikhonov regularization with nonnegativity constraint, which is then minimized by a Kaczmarz type method. In this work, we revisit two issues in the numerical reconstruction in MPI in the lens of inverse theory, i.e. the choice of fidelity and acceleration, and propose two algorithmic tricks, i.e. a whitening procedure to incorporate the noise statistics and accelerating Kaczmarz iteration via randomized SVD. The two tricks are straightforward to implement and easy to incorporate in existing reconstruction algorithms. Their significant potentials are illustrated by extensive numerical experiments on a publicly available dataset.
| Type: | Article |
|---|---|
| Title: | Enhanced reconstruction in magnetic particle imaging by whitening and randomized SVD approximation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1361-6560/ab1a4f |
| Publisher version: | https://doi.org/10.1088/1361-6560/ab1a4f |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL 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/10072149 |
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