Intensity Inhomogeneity Correction of Structural MR Images: A Data-Driven Approach to Define Input Algorithm Parameters

Intensity non-uniformity (INU) in magnetic resonance (MR) imaging is a major issue when conducting analyses of brain structural properties. An inaccurate INU correction may result in qualitative and quantitative misinterpretations. Several INU correction methods exist, whose performance largely depe...

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Published in:Frontiers in neuroinformatics Vol. 10; p. 10
Main Authors: Ganzetti, Marco, Wenderoth, Nicole, Mantini, Dante
Format: Journal Article
Language:English
Published: Switzerland Frontiers Research Foundation 15.03.2016
Frontiers Media S.A
Subjects:
Algorithms
Bias Correction
Bias field
intensity non-uniformity
Magnetic resonance imaging
Medical imaging
Neuroimaging
Neuroscience
NMR
Nuclear magnetic resonance
RF inhomogeneities
Substantia alba
Substantia grisea
Variation
intensity non-uniformity
magnetic resonance imaging
bias correction
bias field
RF inhomogeneities
ISSN:1662-5196, 1662-5196
Online Access:Get full text
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Summary:Intensity non-uniformity (INU) in magnetic resonance (MR) imaging is a major issue when conducting analyses of brain structural properties. An inaccurate INU correction may result in qualitative and quantitative misinterpretations. Several INU correction methods exist, whose performance largely depend on the specific parameter settings that need to be chosen by the user. Here we addressed the question of how to select the best input parameters for a specific INU correction algorithm. Our investigation was based on the INU correction algorithm implemented in SPM, but this can be in principle extended to any other algorithm requiring the selection of input parameters. We conducted a comprehensive comparison of indirect metrics for the assessment of INU correction performance, namely the coefficient of variation of white matter (CVWM), the coefficient of variation of gray matter (CVGM), and the coefficient of joint variation between white matter and gray matter (CJV). Using simulated MR data, we observed the CJV to be more accurate than CVWM and CVGM, provided that the noise level in the INU-corrected image was controlled by means of spatial smoothing. Based on the CJV, we developed a data-driven approach for selecting INU correction parameters, which could effectively work on actual MR images. To this end, we implemented an enhanced procedure for the definition of white and gray matter masks, based on which the CJV was calculated. Our approach was validated using actual T1-weighted images collected with 1.5 T, 3 T, and 7 T MR scanners. We found that our procedure can reliably assist the selection of valid INU correction algorithm parameters, thereby contributing to an enhanced inhomogeneity correction in MR images.
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Reviewed by: Graham J. Galloway, The University of Queensland, Australia; Miriam H. A. Bauer, University of Marburg, Germany
Edited by: Arjen Van Ooyen, Vrije Universiteit Amsterdam, Netherlands
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2016.00010