Greedy Approximate Projection for Magnetic Resonance Fingerprinting with Partial Volumes

Roberto Duarte, Audrey Repetti, Pedro A. Gómez, Mike Davies, Yves Wiaux

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
71 Downloads (Pure)


In quantitative magnetic resonance imaging, traditional methods suffer from the so-called partial volume effect (PVE) due to spatial resolution limitations. As a consequence of PVE, the parameters of the voxels containing more than one tissue are not correctly estimated. Magnetic resonance fingerprinting (MRF) is not an exception. The existing methods addressing PVE are neither scalable nor accurate. We propose to formulate the recovery of multiple tissues per voxel as a non-convex constrained least-squares minimisation problem. To solve this problem, we develop a memory efficient, greedy approximate projected gradient descent algorithm, dubbed GAP-MRF. Our method adaptively finds the regions of interest on the manifold of fingerprints defined by the MRF sequence. We generalise our method to compensate for phase errors appearing in the model, using an alternating minimisation approach. We show, through simulations on synthetic data with PVE, that our algorithm outperforms state-of-the-art methods in reconstruction quality. Our approach is validated on the EUROSPIN phantom and on in vivo datasets.

Original languageEnglish
Article number035015
JournalInverse Problems
Issue number3
Early online date12 Feb 2020
Publication statusPublished - Mar 2020


  • MRI
  • magnetic resonance fingerprinting
  • non-convex optimization
  • partial volume

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Signal Processing
  • Mathematical Physics
  • Computer Science Applications
  • Applied Mathematics


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