Denoising of diffusion MRI using random matrix theory

Veraart, Jelle; Novikov, Dmitry S.; Christiaens, Daan; Ades‐Aron, Benjamin; Sijbers, Jan; Fieremans, Els

doi:10.1016/j.neuroimage.2016.08.016

Cited by 1,277 publications

(1,259 citation statements)

References 60 publications

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“…All image reconstructions used an order 2 Kaiser‐Bessel k‐space filter to suppress Gibbs ringing (an order 3 filter was used for the multiple frequency scan, which had brighter CSF), PCA denoising before receiver combination, and SENSE‐1 coil combination using a direct method that outputs real‐valued signal …”

Section: Methodsmentioning

confidence: 99%

Diffusion dispersion imaging: Mapping oscillating gradient spin‐echo frequency dependence in the human brain

Arbabi

Kai

Khan

et al. 2019

Magnetic Resonance in Med

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Purpose Oscillating gradient spin‐echo (OGSE) diffusion MRI provides information about the microstructure of biological tissues by means of the frequency dependence of the apparent diffusion coefficient (ADC). ADC dependence on OGSE frequency has been explored in numerous rodent studies, but applications in the human brain have been limited and have suffered from low contrast between different frequencies, long scan times, and a limited exploration of the nature of the ADC dependence on frequency. Theory and Methods Multiple frequency OGSE acquisitions were acquired in healthy subjects at 7T to explore the power‐law frequency dependence of ADC, the “diffusion dispersion.” Furthermore, a method for optimizing the estimation of the ADC difference between different OGSE frequencies was developed, which enabled the design of a highly efficient protocol for mapping diffusion dispersion. Results For the first time, evidence of a linear dependence of ADC on the square root of frequency in healthy human white matter was obtained. Using the optimized protocol, high‐quality, full‐brain maps of apparent diffusion dispersion rate were also demonstrated at an isotropic resolution of 2 mm in a scan time of 6 min. Conclusions This work sheds light on the nature of diffusion dispersion in the healthy human brain and introduces full‐brain diffusion dispersion mapping at clinically relevant scan times. These advances may lead to new biomarkers of pathology or improved microstructural modeling.

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Section: Methodsmentioning

confidence: 99%

Diffusion dispersion imaging: Mapping oscillating gradient spin‐echo frequency dependence in the human brain

Arbabi

Kai

Khan

et al. 2019

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

“…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).…”

Section: Methodsmentioning

confidence: 99%

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

Aerts

Schirner

Jeurissen

et al. 2018

eNeuro

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Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong–Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

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“…To examine the effects of DWI signal variance and noise floor, we produced both a “signal‐transformed” version and an “noise‐added” version of the original (eight‐shell) dataset from a single brain. This approach was favored over the more rigorous approach of generating a “noise‐free” DWI dataset as a basis for noise addition, because the use of a single model to generate a noise‐free set would differentially affect the outcomes of each model in a multimodel study—although it should be noted that during preparation of this study, a novel method for model‐free denoising was developed that merits future exploration.…”

Section: Methodsmentioning

confidence: 99%

Analysis of the effects of noise, DWI sampling, and value of assumed parameters in diffusion MRI models

Hutchinson

Avram

İrfanoğlu

et al. 2017

Magnetic Resonance in Med

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PurposeThis study was a systematic evaluation across different and prominent diffusion MRI models to better understand the ways in which scalar metrics are influenced by experimental factors, including experimental design (diffusion‐weighted imaging [DWI] sampling) and noise.MethodsFour diffusion MRI models—diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator MRI (MAP‐MRI), and neurite orientation dispersion and density imaging (NODDI)—were evaluated by comparing maps and histogram values of the scalar metrics generated using DWI datasets obtained in fixed mouse brain with different noise levels and DWI sampling complexity. Additionally, models were fit with different input parameters or constraints to examine the consequences of model fitting procedures.ResultsExperimental factors affected all models and metrics to varying degrees. Model complexity influenced sensitivity to DWI sampling and noise, especially for metrics reporting non‐Gaussian information. DKI metrics were highly susceptible to noise and experimental design. The influence of fixed parameter selection for the NODDI model was found to be considerable, as was the impact of initial tensor fitting in the MAP‐MRI model.ConclusionAcross DTI, DKI, MAP‐MRI, and NODDI, a wide range of dependence on experimental factors was observed that elucidate principles and practical implications for advanced diffusion MRI. Magn Reson Med 78:1767–1780, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Denoising of diffusion MRI using random matrix theory

Cited by 1,277 publications

References 60 publications

Diffusion dispersion imaging: Mapping oscillating gradient spin‐echo frequency dependence in the human brain

Diffusion dispersion imaging: Mapping oscillating gradient spin‐echo frequency dependence in the human brain

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

Analysis of the effects of noise, DWI sampling, and value of assumed parameters in diffusion MRI models

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