Multicompartment magnetic resonance fingerprinting

Sunli Tang, Carlos Fernandez-Granda, Sylvain Lannuzel, Brett Bernstein, Riccardo Lattanzi, Martijn Cloos, Florian Knoll, Jakob Asslander

Research output: Contribution to journalArticlepeer-review


Magnetic resonance fingerprinting (MRF) is a technique for quantitative estimation of spin-relaxation parameters from magnetic-resonance data. Most current MRF approaches assume that only one tissue is present in each voxel, which neglects intravoxel structure, and may lead to artifacts in the recovered parameter maps at boundaries between tissues. In this work, we propose a multicompartment MRF model that accounts for the presence of multiple tissues per voxel. The model is fit to the data by iteratively solving a sparse linear inverse problem at each voxel, in order to express the measured magnetization signal as a linear combination of a few elements in a precomputed fingerprint dictionary. Thresholding-based methods commonly used for sparse recovery and compressed sensing do not perform well in this setting due to the high local coherence of the dictionary. Instead, we solve this challenging sparse-recovery problem by applying reweighted-l1-norm regularization, implemented using an efficient interior-point method. The proposed approach is validated with simulated data at different noise levels and undersampling factors, as well as with a controlled phantom-imaging experiment on a clinical magnetic-resonance system.

Original languageEnglish (US)
Article number094005
JournalInverse Problems
Issue number9
StatePublished - Jul 24 2018


  • coherent dictionaries
  • magnetic resonance fingerprinting
  • multicompartment models
  • quantitative MRI
  • reweighted l1-norm
  • sparse recovery

ASJC Scopus subject areas

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


Dive into the research topics of 'Multicompartment magnetic resonance fingerprinting'. Together they form a unique fingerprint.

Cite this