Fast, automated, N‐dimensional phase‐unwrapping algorithm

By Mark Jenkinson. Magnetic Resonance in Medicine, 2003

772 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Molecular Biology, Radiation

View Paper: https://www.semanticscholar.org/paper/26fe11930565f244174869077411869097c96dd3

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Citations in the corpus

Real-time structured light profilometry: a review (2016, background): “Although 439 multi-dimensional expansion increases the complexity of the problem, many of the core 440 principles upon which two-dimensional phase unwrapping algorithms are based can be 441 transferred to solve the problem in N dimensions [103,104].”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, methodology): “tion correction [9], and distortion correction [10], [11] are done prior to statistical analysis and functional localization procedures.”
How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging (2003, methodology): “In addition, unwrapping was compared to that using a completely independent method (Jenkinson, 2003) to ensure that there were no wrapping errors.”
Bayesian analysis of neuroimaging data in FSL (2009, methodology): “…Smith et al., 2006), FLIRT (FMRIB's linear image registration tool; Jenkinson and Smith, 2001), PRELUDE/FUGUE (phase unwarping and MRI unwarping; Jenkinson, 2003), and the “unmixing” algorithm at the heart of the MELODIC ICA-based approach for model-free FMRI analysis (Beckmann and Smith, 2004,…”
Fast and robust three-dimensional best path phase unwrapping algorithm. (2007, methodology): “This method has been designed to process 2D and 3D MRI data, but it can be extended to permit the unwrapping of N-dimensional data [14].”
Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres (2015, methodology): “Pre-processing of diffusion MRIs included EPI correction (Jenkinson, 2003), motion correction (Raffelt et al., 2012c), bias ﬁ eld correction based on the b = 0 image (Tustison et al., 2010), and up-sampling by a factor of 2 using b-spline interpolation (Raffelt et al., 2012b).”
Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism? (2011, methodology): “If phase aliasing is present in GRE data, it can be resolved, e.g., through application of an unwrapping algorithm (Abdul-Rahman et al., 2007; Jenkinson, 2003; Witoszynskyj et al., 2009).”
Advances in functional and structural MR image analysis and implementation as FSL. (2004, methodology): “A general N-dimensional phase-unwrapping technique ( Jenkinson, 2003 ) was developed for this task based on optimizing a global cost function that penalizes large, spatially localized phase changes.”
Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images (2012, methodology): “Correction was performed using Fugue (Jenkinson, 2003) and Prelude (Jenkinson, 2004) which are part of the FSL toolkit.”
Methods for cleaning the BOLD fMRI signal (2016, positive): “As a first step, phase wraps can be efficiently removed by phase unwrapping methods to improve phase stability (Cusack and Papadakis, 2002; Hagberg et al., 2008; Jenkinson, 2003; Tomasi and Caparelli, 2007).”
Frontal circuitry degradation marks healthy adult aging: Evidence from diffusion tensor imaging (2005, methodology): “Using the field maps, B 0 -field inhomogeneity-induced geometric distortion in the eddy currentcorrected images was corrected with FUGUE (FMRIB's Utility for Geometrically Unwarping EPIs) (Jenkinson, 2003).”
Model-based analysis of multi-shell diffusion MR data for tractography: How to get over fitting problems (2012, methodology): “Field mapbased echo planar imaging (EPI) distortion correction was applied using FUGUE (28).”
MRI Estimates of Brain Iron Concentration in Normal Aging Using Quantitative Susceptibility Mapping (2011, methodology): “Phase images were constructed from the real and imaginary components of the SWI-SPGR data after the phase had been unwrapped with FSL PRELUDE (Phase Region Expanding Labeler for Unwrapping Discrete Estimates (Jenkinson, 2003)).”