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Multiecho complex total field inversion method (mcTFI) for improved signal modeling in quantitative susceptibility mapping

  • Yan Wen
  • , Pascal Spincemaille
  • , Thanh Nguyen
  • , Junghun Cho
  • , Ilhami Kovanlikaya
  • , Julie Anderson
  • , Gaohong Wu
  • , Baolian Yang
  • , Maggie Fung
  • , Ke Li
  • , Douglas Kelley
  • , Nissim Benhamo
  • , Yi Wang
  • Cornell University
  • GE Healthcare United States

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Purpose: Typical quantitative susceptibility mapping (QSM) reconstruction steps consist of first estimating the magnetization field from the gradient-echo images, and then reconstructing the susceptibility map from the estimated field. The errors from the field-estimation steps may propagate into the final QSM map, and the noise in the estimated field map may no longer be zero-mean Gaussian noise, thus, causing streaking artifacts in the resulting QSM. A multiecho complex total field inversion (mcTFI) method was developed to compute the susceptibility map directly from the multiecho gradient echo images using an improved signal model that retains the Gaussian noise property in the complex domain. It showed improvements in QSM reconstruction over the conventional field-to-source inversion. Methods: The proposed mcTFI method was compared with the nonlinear total field inversion (nTFI) method in a numerical brain with hemorrhage and calcification, the numerical brains provided by the QSM Challenge 2.0, 18 brains with intracerebral hemorrhage scanned at 3T, and 6 healthy brains scanned at 7T. Results: Compared with nTFI, the proposed mcTFI showed more accurate QSM reconstruction around the lesions in the numerical simulations. The mcTFI reconstructed QSM also showed the best image quality with the least artifacts in the brains with intracerebral hemorrhage scanned at 3T and healthy brains scanned at 7T. Conclusion: The proposed multiecho complex total field inversion improved QSM reconstruction over traditional field-to-source inversion through better signal modeling.

Original languageEnglish
Pages (from-to)2165-2178
Number of pages14
JournalMagnetic Resonance in Medicine
Volume86
Issue number4
DOIs
StatePublished - Oct 2021

Keywords

  • brain imaging
  • nonlinear total field inversion
  • quantitative susceptibility mapping

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