Gibbs‐ringing artifact removal based on local subvoxel‐shifts

Abstract

Purpose: To develop a fast and stable method for correcting the gibbs-ringing artifact. Methods: Gibbs-ringing is a well-known artifact which manifests itself as spurious oscillations in the vicinity of sharp image gradients at tissue boundaries. The origin can be seen in the truncation of k-space during MRI data-acquisition. Correction techniques like Gegenbauer reconstruction or extrapolation methods aim at recovering these missing data. Here, we present a simple and robust method which exploits a different view on the Gibbs-phenomenon: The truncation in k-space can be interpreted as a convolution of the underlying image with a sinc-function. As the image is reconstructed on a discretized grid, the severity of the ringing artifacts depends on how this grid is located with respect to the edge and the oscillation pattern of the function. We propose to reinterpolate the image based on local, subvoxel-shifts to sample the ringing pattern at the zero-crossings of the oscillating sinc-function. Results: With the proposed method, the artifact can simply, effectively, and robustly be removed with a minimal amount of image smoothing. Conclusions: The robustness of the method suggests it as a suitable candidate for an implementation in the standard image processing pipeline in clinical routine. Magn Reson

Denoising of diffusion MRI using random matrix theory

“…smoothing or total variation minimization (Block et al, 2008; Veraart et al, 2015; Perrone et al, 2015), that might deal with different types of unwanted fluctuations simultaneously, we here advocate the use of targeted artifact correction techniques for improved accuracy and specificity. Other examples of targeted image processing tools are the Gibbs correction framework of Kellner et al (2015) or FSL’s TOPUP and EDDY for EPI and eddy current distortion corrections, respectively (Smith et al, 2004; Sotiropoulos et al, 2013; Glasser et al, 2013). Note, however, that denoising should be applied as the first step of the processing pipeline because data interpolation or smoothing will change the noise characteristics on which MPPCA relies.…”

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

“…First, raw diffusion-weighted MRI images were corrected for several artifacts. In particular, DWI images were denoised (MRtrix dwidenoise; Veraart et al, 2016) and corrected for Gibbs ringing artifacts (MRtrix mrdegibbs; Kellner et al, 2016), for motion and eddy currents (FSL eddy; Andersson and Sotiropoulos, 2016), for susceptibility-induced distortions (FSL topup; Andersson et al, 2003), and for bias field inhomogeneities (FSL FAST; Zhang et al, 2001). Next, subjects’ high-resolution anatomic images were linearly registered to diffusion space with the epi_reg function of FSL FLIRT (Jenkinson and Smith, 2001; Jenkinson et al, 2002) and segmented into gray matter, white matter, and cerebrospinal fluid (FSL FAST; Zhang et al, 2001).…”

Modeling white matter tract integrity in aging with diffusional kurtosis imaging

“…All DKI acquisitions were denoised with a principal components analysis technique (Veraart et al, 2016b) and Gibbs ringing artifact reduction (Kellner et al, 2016; Veraart et al, 2016a) prior to additional processing utilizing in-house software (diffusional kurtosis estimator (Tabesh et al, 2011)), for registration and estimation of the diffusion and kurto- sis tensors. Five of 54 baseline and 4 of 39 follow-up DKI acquisi- tions had 1e3 volumes with signal dropouts due to motion, and therefore, these volume directions were excluded from the calcu- lation of parametric maps.…”