Accelerated Parameter Mapping of Multiple-Echo Gradient-Echo Data Using Model-Based Iterative Reconstruction

Markus Zimmermann, Zaheer Abbas, Krzysztof Dzieciol, N Jon Shah

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

A new reconstruction method, coined MIRAGE, is presented for accurate, fast, and robust parameter mapping of multiple-echo gradient-echo (MEGE) imaging, the basis sequence of novel quantitative magnetic resonance imaging techniques such as water content and susceptibility mapping. Assuming that the temporal signal can be modeled as a sum of damped complex exponentials, MIRAGE performs model-based reconstruction of undersampled data by minimizing the rank of local Hankel matrices. It further incorporates multi-channel information and spatial prior knowledge. Finally, the parameter maps are estimated using nonlinear regression. Simulations and retrospective undersampling of phantom and in vivo data affirm robustness, e.g., to strong inhomogeneity of the static magnetic field and partial volume effects. MIRAGE is compared with a state-of-the-art compressed sensing method, L-1-ESPIRiT. Parameter maps estimated from reconstructed data using MIRAGE are shown to be accurate, with the mean absolute error reduced by up to 50% for in vivo results. The proposed method has the potential to improve the diagnostic utility of quantitative imaging techniques that rely on MEGE data.

Original languageEnglish
Pages (from-to)626-637
Number of pages12
JournalIeee Transactions on Medical Imaging
Volume37
Issue number2
DOIs
Publication statusPublished - Feb 2018
Externally publishedYes

Keywords

  • ALGORITHM
  • BRAIN
  • FIELD
  • Inverse methods
  • MRI
  • OPTIMIZATION
  • SCLEROSIS
  • SUSCEPTIBILITY
  • SYSTEM
  • UNDERSAMPLED DATA
  • WATER-CONTENT
  • low rank Hankel matrix
  • magnetic resonance imaging (MRI)
  • model-based iterative reconstruction
  • multiple-echo gradient-echo
  • parameter mapping

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