.png){kind=icon}
Return
Fast nonlinear susceptibility inversion with variational regularization
.png){kind=icon}
OA
AI
DOI：10.1002/mrm.27073.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
PurposeQuantitative susceptibility mapping can be performed through the minimization of a function consisting of data fidelity and regularization terms. For data consistency, a Gaussian-phase noise distribution is often assumed, which breaks down when the signal-to-noise ratio is low. A previously proposed alternative is to use a nonlinear data fidelity term, which reduces streaking artifacts, mitigates noise amplification, and results in more accurate susceptibility estimates. We hereby present a novel algorithm that solves the nonlinear functional while achieving computation speeds comparable to those for a linear formulation. MethodsWe developed a nonlinear quantitative susceptibility mapping algorithm (fast nonlinear susceptibility inversion) based on the variable splitting and alternating direction method of multipliers, in which the problem is split into simpler subproblems with closed-form solutions and a decoupled nonlinear inversion hereby solved with a Newton-Raphson iterative procedure. Fast nonlinear susceptibility inversion performance was assessed using numerical phantom and in vivo experiments, and was compared against the nonlinear morphology-enabled dipole inversion method. ResultsFast nonlinear susceptibility inversion achieves similar accuracy to nonlinear morphology-enabled dipole inversion but with significantly improved computational efficiency. ConclusionThe proposed method enables accurate reconstructions in a fraction of the time required by state-of-the-art quantitative susceptibility mapping methods. Magn Reson Med 80:814-821, 2018. (c) 2018 International Society for Magnetic Resonance in Medicine.
Keywords:
nonlinear inversion
quantitative susceptibility mapping
total variation
augmented Lagrangian
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
1.2W
Citations:
3.1W
Organization
View more organizations
Cited Papers
Cost-effectiveness of a quality improvement programme to reduce central line-associated bloodstream infections in intensive care units in the USA.png)
BMJ OpenOAAI
BMJ OpenIF0
OAAIShareSave
Fast Quantitative Susceptibility Mapping with L1-Regularization and Automatic Parameter SelectionOAAI
OAAIShareSaveShareSave View more