Simultaneous estimation of PD, T1 , T2 , T2* , and ∆B0 using magnetic resonance fingerprinting with background gradient compensation

Magn Reson Med. 2019 Apr;81(4):2614-2623. doi: 10.1002/mrm.27556. Epub 2018 Nov 13.

Abstract

Purpose: This study aims to estimate PD, T1 , T2 , T2* , and Δ B0 simultaneously using magnetic resonance fingerprinting (MRF) with compensation of the linearly varying background field.

Methods: MRF based on fast imaging with steady-state precession (FISP) and multi-echo spoiled gradient (SPGR) schemes are alternatively used, which encode T2 and T2* , respectively. Simulations are performed to determine the appropriate ratio of the FISP and SPGR sections with respect to the T2 and T2* accuracy. Additionally, background field inhomogeneity (Gz ) compensation using z-shim gradients are incorporated into the SPGR section and the dictionary. The background field compensation is tested in the phantom experiment under well-shimmed and poor-shimmed conditions. An in vivo experiment is performed and the estimated parameters are compared before and after Gz compensation.

Results: The T1 , T2 , and T2* values from the phantom results are in good agreement with the reference methods under well-shimmed condition. The underestimated T2 and T2* values under poor-shimmed condition are recovered by Gz compensation and the parameters are also in good agreement with the reference methods. In the human brain, T2 and T2* values are restored by Gz compensation in regions where the magnetic field is particularly inhomogeneous, such as near the sinus and ear canals.

Conclusions: The proposed FISP and SPGR combined MRF provides a simultaneous estimation of PD, T1 , T2 , T2* , and Δ B0 . By incorporating field inhomogeneity as a gradient term into both the sequence and dictionary, T2 and T2* values can be restored where field inhomogeneity exists.

Keywords: MR fingerprinting; T2* measurement; background field compensation; quantitative imaging.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Brain / diagnostic imaging*
  • Computer Simulation
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Magnetic Resonance Imaging / methods*
  • Pattern Recognition, Automated / methods
  • Phantoms, Imaging
  • Signal-To-Noise Ratio
  • Software