Abstract
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.
Highlights
Magnetic Resonance Imaging (MRI) is a technique that delivers detailed images of the human body by analyzing its interactions with radio waves superimposed on a strong magnetic field
For CJV, coefficient of variation of white matter (CVWM), and coefficient of variation of gray matter (CVGM) calculated on low-dynamic profile magnetic resonance (MR) images, D was 0.6, 1.4, and 1.1%, respectively
To show the potential usefulness of the developed datadriven approach to estimate intensity non-uniformity (INU) correction parameters, we used actual MR images collected with 1.5 T, 3 T, and 7 T scanners, respectively
Summary
Magnetic Resonance Imaging (MRI) is a technique that delivers detailed images of the human body by analyzing its interactions with radio waves superimposed on a strong magnetic field. The main factors that can influence the magnitude and spatial profile of the INU include: static field strength, reduced RF coil uniformity, RF penetration, gradient-driven eddy currents, inhomogeneous reception sensitivity profile, and overall subject anatomy and position (Simmons et al, 1994; Mihara et al, 1998; Belaroussi et al, 2006; Vovk et al, 2007).
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