eprintid: 10041770
rev_number: 39
eprint_status: archive
userid: 608
dir: disk0/10/04/17/70
datestamp: 2018-01-26 16:02:43
lastmod: 2021-12-19 00:00:11
status_changed: 2018-03-09 11:09:37
type: article
metadata_visibility: show
creators_name: Milovic, C
creators_name: Bilgic, B
creators_name: Zhao, B
creators_name: Acosta-Cabronero, J
creators_name: Tejos, C
title: Fast nonlinear susceptibility inversion with variational regularization
ispublished: pub
divisions: UCL
divisions: B02
divisions: C07
divisions: B04
divisions: C05
divisions: F42
keywords: Augmented Lagrangian, nonlinear inversion, quantitative susceptibility mapping, total variation
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: PURPOSE: Quantitative 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. METHODS: We 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. RESULTS: Fast nonlinear susceptibility inversion achieves similar accuracy to nonlinear morphology-enabled dipole inversion but with significantly improved computational efficiency. CONCLUSION: The proposed method enables accurate reconstructions in a fraction of the time required by state-of-the-art quantitative susceptibility mapping methods. Magn Reson Med, 2018. © 2018 International Society for Magnetic Resonance in Medicine.
date: 2018-08
date_type: published
official_url: http://dx.doi.org/10.1002/mrm.27073
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
article_type_text: Journal Article
verified: verified_manual
elements_id: 1525587
doi: 10.1002/mrm.27073
lyricists_name: Acosta-Cabronero, Julio
lyricists_name: Milovic, Carlos
lyricists_id: JACOS66
lyricists_id: CMILO93
actors_name: Acosta-Cabronero, Julio
actors_id: JACOS66
actors_role: owner
full_text_status: public
publication: Magnetic Resonance in Medicine
volume: 80
number: 2
pagerange: 814-821
event_location: United States
issn: 1522-2594
citation:        Milovic, C;    Bilgic, B;    Zhao, B;    Acosta-Cabronero, J;    Tejos, C;      (2018)    Fast nonlinear susceptibility inversion with variational regularization.                   Magnetic Resonance in Medicine , 80  (2)   pp. 814-821.    10.1002/mrm.27073 <https://doi.org/10.1002/mrm.27073>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10041770/1/Milovic_Fast_nonlinear_susceptibility.pdf