A compressed sensing framework for magnetic resonance fingerprinting

Mike Davies, Gilles Puy, Pierre Vandergheynst, Yves Wiaux

Research output: Contribution to journalArticlepeer-review

83 Citations (Scopus)
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Inspired by the recently proposed Magnetic Resonance Fingerprinting (MRF) technique we develop a principled compressed sensing framework for quantitative MRI. The three key components are: a random pulse excitation sequence following the MRF technique; a random EPI subsampling strategy and an iterative projection algorithm that imposes consistency with the Bloch equations. We show that, as long as the excitation sequence possesses an appropriate form of persistent excitation, we are able to achieve accurate recovery the proton density, T1, T2 and off-resonance maps simultaneously from a limited number of samples.
Original languageEnglish
Pages (from-to)2623-2656
JournalSIAM Journal on Imaging Sciences
Issue number4
Publication statusPublished - 2014


  • compressed sensing; quantitative MR imaging


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