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Conference Papers Year : 2020

Dictionary-based Learning in MR Fingerprinting: Statistical Learning versus Deep Learning

Abstract

In MR Fingerprinting, the exhaustive search in the dictionary may be bypassed by learning a mapping between fingerprints and parameter spaces. In general, the relationship between these spaces is particularly non-linear, which implies the use of advanced regression methods: deep learning frameworks but also methods based on statistical models have been proposed. In this study, we compare reconstruction time, accuracy and noise robustness of the conventional dictionary-matching method and two methods that handle the modelling of the non-linear relashionship with a neural network and a statistical inverse regression model.
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Dates and versions

hal-02922858 , version 1 (26-08-2020)

Identifiers

  • HAL Id : hal-02922858 , version 1

Cite

Fabien Boux, Florence Forbes, Julyan Arbel, Aurélien Delphin, Thomas Christen, et al.. Dictionary-based Learning in MR Fingerprinting: Statistical Learning versus Deep Learning. ISMRM 2020 - International Society for Magnetic Resonance in Medicine, Aug 2020, Sidney, Australia. pp.1-4. ⟨hal-02922858⟩
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