Singh, Imraj;
Jaubert, Olivier;
Jin, Bangti;
Thielemans, Kris;
Arridge, Simon;
(2022)
Magnetic Resonance Fingerprinting with Total Nuclear Variation Regularisation.
Presented at: Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, London, UK.
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Abstract
Magnetic Resonance Fingerprinting (MRF) accelerates quantitative magnetic resonance imaging. The reconstruction can be separated into two problems: reconstruction of a set of multi-contrast images from k-space signals, and estimation of parametric maps from the set of multi-contrast images. In this study we focus on the former problem, while leveraging dictionary matching for the estimation of parametric maps. Two different sparsity promoting regularisation strategies were investigated: contrast-wise Total Variation (TV) which encourages image sparsity separately; and Total Nuclear Variation (TNV) which promotes a measure of joint edge sparsity. We found improved results using joint sparsity.
| Type: | Conference item (Presentation) |
|---|---|
| Title: | Magnetic Resonance Fingerprinting with Total Nuclear Variation Regularisation |
| Event: | Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting |
| Location: | London, UK |
| Dates: | 07 - 12 May 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://archive.ismrm.org/2022/2515.html |
| Language: | English |
| Keywords: | Magnetic Resonance Fingerprinting, Synergistic Reconstruction, Low-Rankness |
| UCL classification: | 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 UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10156316 |
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