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Fast nonlinear susceptibility inversion with variational regularization.

Carlos Milovic1,2, Berkin Bilgic3, Bo Zhao3

  • 1Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile.

Magnetic Resonance in Medicine
|January 12, 2018
PubMed
Summary
This summary is machine-generated.

A new nonlinear quantitative susceptibility mapping algorithm offers accurate results comparable to existing methods but with significantly faster computation. This breakthrough in magnetic resonance imaging accelerates image reconstruction times.

Keywords:
augmented Lagrangiannonlinear inversionquantitative susceptibility mappingtotal variation

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Science

Background:

  • Quantitative susceptibility mapping (QSM) typically assumes Gaussian-phase noise, which is inaccurate at low signal-to-noise ratios.
  • Existing nonlinear QSM methods improve accuracy but are computationally intensive.

Purpose of the Study:

  • To develop a novel, computationally efficient nonlinear quantitative susceptibility mapping algorithm.
  • To address limitations of linear assumptions in QSM under low signal-to-noise conditions.

Main Methods:

  • Developed a fast nonlinear susceptibility inversion (FNSI) algorithm using variable splitting and the alternating direction method of multipliers.
  • Solved the nonlinear functional with a Newton-Raphson iterative procedure.
  • Validated FNSI using numerical phantoms and in vivo experiments, comparing it to nonlinear morphology-enabled dipole inversion (NMEDI).

Main Results:

  • FNSI achieved accuracy comparable to NMEDI.
  • FNSI demonstrated significantly improved computational efficiency over NMEDI.

Conclusions:

  • The proposed FNSI method enables accurate QSM reconstructions.
  • FNSI significantly reduces the time required for QSM compared to current state-of-the-art methods.