The effect of Gibbs ringing artifacts on measures derived from diffusion MRI

Perrone, Daniele; Aelterman, Jan; Pižurica, Aleksandra; Jeurissen, Ben; Philips, Wilfried; Leemans, Alexander

doi:10.1016/j.neuroimage.2015.06.068

Cited by 100 publications

(87 citation statements)

References 121 publications

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“…Despite the convenience of a brute force technique, e.g. 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).…”

Section: Discussionmentioning

confidence: 99%

“…Limitations are the dependency on a regularization term, introduction of reconstruction artifacts, and the fact that thermal noise is not the sole source of local variations. Indeed, fine anatomical details might be removed as well by this non-selective technique (Block et al, 2008; Knoll et al, 2011; Veraart et al, 2015; Perrone et al, 2015). …”

Section: Introductionmentioning

confidence: 99%

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Denoising of diffusion MRI using random matrix theory

et al. 2016

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We introduce and evaluate a post-processing technique for fast denoising diffusion-weighted MR images. By exploiting the intrinsic redundancy in diffusion MRI using universal properties of the eigenspectrum of random covariance matrices, we remove noise-only principal components, thereby enabling signal-to-noise ratio enhancements, yielding parameter maps of improved quality for visual, quantitative, and statistical interpretation. By studying statistics of residuals, we demonstrate that the technique suppresses local signal fluctuations that solely originate from thermal noise rather than from other sources such as anatomical detail. Furthermore, we achieve improved precision in the estimation of diffusion parameters and fiber orientations in the human brain without compromising the accuracy and/or spatial resolution.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Denoising of diffusion MRI using random matrix theory

et al. 2016

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“…This was verified during the ROI delineation procedure. Recent studies have shown the presence of Gibbs ringing in d-MRI data to severely impact parameter estimation and have demonstrated a number of strategies to suppress the artifact prior to analysis (Kellner et al, 2015; Perrone et al, 2015; Veraart et al, 2015). In the present study, Gaussian low pass filtering was employed for smoothing the data to reduce the Gibbs ringing artefacts, similar to previously published methods (Tabesh et al, 2011; Veraart et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

Biophysical modeling of high field diffusion MRI demonstrates micro-structural aberration in chronic mild stress rat brain

et al. 2016

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Depression is one of the leading causes of disability worldwide. Immense heterogeneity in symptoms of depression causes difficulty in diagnosis, and to date, there are no established biomarkers or imaging methods to examine depression. Unpredictable chronic mild stress (CMS) induced anhedonia is considered to be a realistic model of depression in studies of animal subjects. Stereological and neuronal tracing techniques have demonstrated persistent remodeling of microstructure in hippocampus, prefrontal cortex and amygdala of CMS brains. Recent developments in diffusion MRI (d-MRI) analyses, such as neurite density and diffusion kurtosis imaging (DKI), are able to capture microstructural changes and are considered to be robust tools in preclinical and clinical imaging. The present study utilized d-MRI analyzed with a neurite density model and the DKI framework to investigate microstructure in the hippocampus, prefrontal cortex, caudate putamen and amygdala regions of CMS rat brains by comparison to brains from normal controls. To validate findings of CMS induced microstructural alteration, histology was performed to determine neurite, nuclear and astrocyte density. d-MRI based neurite density and tensor-based mean kurtosis (MKT) were significantly higher, while mean diffusivity (MD), extracellular diffusivity (Deff) and intra-neurite diffusivity(DL) were significantly lower in the amygdala of CMS rat brains. Deff was also significantly lower in the hippocampus and caudate putamen in stressed groups. Histological neurite density corroborated the d-MRI findings in the amygdala and reductions in nuclear and astrocyte density further buttressed the d-MRI results. The present study demonstrated that the d-MRI based neurite density and MKT can reveal specific microstructural changes in CMS rat brains and these parameters might have value in clinical diagnosis of depression and for evaluation of treatment efficacy.

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“…DSI, on the other hand, uses a large number of diffusion encoding vectors to characterize diffusion dynamics, which could have lower angular variability from the dODF reconstruction but an increased likelihood for subject motion. DKI is also known to be sensitive to reconstruction artifacts resulting from Gibbs ringing (34,35) and noise bias (36), although these are also expected to affect DSI.…”

Section: Discussionmentioning

confidence: 99%

“…In general, DKI substantially decreases the error of dODF orientation estimates relative to DTI. Moreover, DKI enables the detection of crossing fibers, which results in pronounced improvements relative to DTI for tractography throughout regions with complex fiber bundle geometries (15,16,32,35). Indeed, our results indicate that the tractography obtained with DKI is qualitatively quite comparable to that for DSI, in spite of DKI sampling a much smaller portion of q-space.…”

Section: Discussionmentioning

confidence: 99%

Mapping the Orientation of White Matter Fiber Bundles: A Comparative Study of Diffusion Tensor Imaging, Diffusional Kurtosis Imaging, and Diffusion Spectrum Imaging

et al. 2016

American Journal of Neuroradiology

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Background and Purpose White matter fiber tractography relies on fiber bundle orientation estimates from diffusion MRI. However, clinically feasible techniques such as DTI and DKI utilize assumptions, which may introduce error into in vivo orientation estimates. In this study, fiber bundle orientations from DTI and DKI are compared to DSI as a gold standard to assess the performance of each technique. Materials and Methods For each subject, full DTI, DKI, and DSI datasets were acquired during two independent sessions, and fiber bundle orientations were estimated using the specific theoretical assumptions of each technique. Angular variability and angular error measures were assessed by comparing the orientation estimates. Tractography generated with each of the three reconstructions was also examined and contrasted. Results Orientation estimates from all three techniques had comparable angular reproducibility, but DKI decreased angular error throughout the white matter compared to DTI. DSI and DKI enabled the detection of crossing fiber bundles, which had pronounced effects on tractography relative to DTI. DSI had the highest sensitivity for detecting crossing fibers; however, the DSI and DKI tracts were qualitatively similar. Conclusion Fiber bundle orientation estimates from DKI have less systematic error than those from DTI, which can significantly affect tractography. Moreover, tractography obtained with DKI is qualitatively comparable to that of DSI. Since DKI has a shorter typical scan time than DSI, DKI is potentially more suitable for a variety of clinical and research applications.

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The effect of Gibbs ringing artifacts on measures derived from diffusion MRI

Cited by 100 publications

References 121 publications

Denoising of diffusion MRI using random matrix theory

Denoising of diffusion MRI using random matrix theory

Biophysical modeling of high field diffusion MRI demonstrates micro-structural aberration in chronic mild stress rat brain

Mapping the Orientation of White Matter Fiber Bundles: A Comparative Study of Diffusion Tensor Imaging, Diffusional Kurtosis Imaging, and Diffusion Spectrum Imaging

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