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Abstract #2515

Magnetic Resonance Fingerprinting with Total Nuclear Variation Regularisation

Imraj Ravi Devia Singh1, Olivier Jaubert1, Bangti Jin1, Kris Thielemans2, and Simon Arridge1
1Department of Computer Science, University College London, London, United Kingdom, 2Institute of Nuclear Medicine, University College London, London, United Kingdom


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.

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