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

Yan Wen1,2, Pascal Spincemaille2, Thanh Nguyen2

  • 1Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.

Magnetic Resonance in Medicine
|May 24, 2021
PubMed
Summary

A new multiecho complex total field inversion (mcTFI) method improves quantitative susceptibility mapping (QSM) by directly reconstructing susceptibility maps. This approach reduces artifacts and enhances image quality compared to traditional methods.

Keywords:
brain imagingnonlinear total field inversionquantitative susceptibility mapping

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Neuroscience

Background:

  • Quantitative susceptibility mapping (QSM) typically involves two steps: field estimation and susceptibility map reconstruction.
  • Conventional QSM methods can propagate errors and introduce artifacts due to noise properties in the estimated field map.

Purpose of the Study:

  • To develop and evaluate a novel multiecho complex total field inversion (mcTFI) method for direct QSM reconstruction.
  • To improve the accuracy and image quality of QSM by addressing limitations in traditional field-to-source inversion.

Main Methods:

  • The mcTFI method utilizes an improved signal model to directly compute susceptibility maps from multiecho gradient echo images.
  • The mcTFI method was validated against the nonlinear total field inversion (nTFI) method using numerical brain models and clinical datasets (3T and 7T MRI scans).

Main Results:

  • mcTFI demonstrated more accurate QSM reconstruction around simulated lesions compared to nTFI.
  • QSM reconstructed with mcTFI exhibited superior image quality with reduced artifacts in human brain scans, including those with intracerebral hemorrhage.

Conclusions:

  • The mcTFI method offers improved QSM reconstruction over traditional field-to-source techniques.
  • Enhanced signal modeling in mcTFI preserves Gaussian noise properties, leading to better QSM outcomes.