Carlos Milovic1,2, Berkin Bilgic3, Bo Zhao3, Julio Acosta-Cabronero4, Cristian Tejos1,2. 1. Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile. 2. Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile. 3. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts, USA. 4. Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.
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 80:814-821, 2018.
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 80:814-821, 2018.
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Authors: Carlos Milovic; Cristian Tejos; Julio Acosta-Cabronero; Pinar Senay Özbay; Ferdinand Schwesser; Jose Pedro Marques; Pablo Irarrazaval; Berkin Bilgic; Christian Langkammer Journal: Magn Reson Med Date: 2020-02-21 Impact factor: 4.668
Authors: José P Marques; Jakob Meineke; Carlos Milovic; Berkin Bilgic; Kwok-Shing Chan; Renaud Hedouin; Wietske van der Zwaag; Christian Langkammer; Ferdinand Schweser Journal: Magn Reson Med Date: 2021-02-26 Impact factor: 4.668
Authors: Julio Acosta-Cabronero; Carlos Milovic; Hendrik Mattern; Cristian Tejos; Oliver Speck; Martina F Callaghan Journal: Neuroimage Date: 2018-07-31 Impact factor: 6.556