Local estimation of the noise level in MRI using structural adaptation

Tabelow, Karsten; Voss, Henning U.; Polzehl, Jörg

doi:10.1016/j.media.2014.10.008

Cited by 21 publications

(21 citation statements)

References 40 publications

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“…To classify scans based on the noise floor, we used the σ (standard deviation of the MRI signal), calculated with the following formula:

, where

is the standard deviation of the magnitude–reconstructed cerebrospinal fluid (CSF) signal ( Jones and Basser, 2004 , Tabelow et al, 2015 , Battiston et al, 2018 ). In particular, the CSF ring surrounding the spinal cord was derived by dilation of the spinal cord mask by 2-pixel layer, and, then, by subsequent subtraction of the mask once; any value of voxels within the extracted ring that were >2 standard deviations above the mean were discarded to avoid the inclusion of values from nerve roots or other spurious signal intensities ( Prados et al, 2020 , Zhou et al, 2018 , Sakaie et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

Spinal cord atrophy in a primary progressive multiple sclerosis trial: Improved sample size using GBSI

Moccia

Valsecchi

Ciccarelli

et al. 2020

NeuroImage: Clinical

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“…To classify scans based on the noise floor, we used the σ (standard deviation of the MRI signal), calculated with the following formula:

, where

Section: Methodsmentioning

confidence: 99%

Spinal cord atrophy in a primary progressive multiple sclerosis trial: Improved sample size using GBSI

Moccia

Valsecchi

Ciccarelli

et al. 2020

NeuroImage: Clinical

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“…Different levels of spatially homogeneous and inhomogeneous Rician noise were simulated by adding white Gaussian noise to the real and imaginary parts of the DW images resulting in a SNR range of = 20–100 in the non-DW images. Spatially varying noise distributions were generated similar to those described by Tabelow et al [32]. In the simulations, the noise standard deviation was set relative to the joint average of the signals of gray and white matter in the non-DW images of the brain phantom.…”

Section: Methodsmentioning

confidence: 99%

Image denoising substantially improves accuracy and precision of intravoxel incoherent motion parameter estimates

Reischauer

Gutzeit

2017

PLoS ONE

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Applicability of intravoxel incoherent motion (IVIM) imaging in the clinical setting is hampered by the limited reliability in particular of the perfusion-related parameter estimates. To alleviate this problem, various advanced postprocessing methods have been introduced. However, the underlying algorithms are not readily available and generally suffer from an increased computational burden. Contrary, several computationally fast image denoising methods have recently been proposed which are accessible online and may improve reliability of IVIM parameter estimates. The objective of the present work is to investigate the impact of image denoising on accuracy and precision of IVIM parameter estimates using comprehensive in-silico and in-vivo experiments. Image denoising is performed with four different algorithms that work on magnitude data: two algorithms which are based on nonlocal means (NLM) filtering, one algorithm that relies on local principal component analysis (LPCA) of the diffusion-weighted images, and another algorithms that exploits joint rank and edge constraints (JREC). Accuracy and precision of IVIM parameter estimates is investigated in an in-silico brain phantom and an in-vivo ground truth as a function of the signal-to-noise ratio for spatially homogenous and inhomogenous levels of Rician noise. Moreover, precision is evaluated using bootstrap analysis of in-vivo measurements. In the experiments, IVIM parameters are computed a) by using a segmented fit method and b) by performing a biexponential fit of the entire attenuation curve based on nonlinear least squares estimates. Irrespective of the fit method, the results demonstrate that reliability of IVIM parameter estimates is substantially improved by image denoising. The experiments show that the LPCA and the JREC algorithms perform in a similar manner and outperform the NLM-related methods. Relative to noisy data, accuracy of the IVIM parameters in the in-silico phantom improves after image denoising by 76–79%, 79–81%, 84–99% and precision by 74–80%, 80–83%, 84–95% for the perfusion fraction, the diffusion coefficient, and the pseudodiffusion coefficient, respectively, when the segmented fit method is used. Beyond that, the simulations reveal that denoising performance is not impeded by spatially inhomogeneous levels of Rician noise in the image. Since all investigated algorithms are freely available and work on magnitude data they can be readily applied in the clinical setting which may foster transition of IVIM imaging into clinical practice.

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“…So far, only a few noise estimation methods are able to deal with the spatially varying nature of noise . The existing techniques depend on (i) the assumption of homogeneous signal intensities in a local neighborhood ; (ii) diffusion model assumptions ; (iii) repeated measurements that are often not available ; or (iv) some way of decomposing images into “low frequency” (signal) and “high frequency” (noise), e.g., using wavelets , which tends to overestimate the noise level, as sharp edges contribute to the high frequency sub‐band. Physiological noise, image misalignment, and model inaccuracies, all tend to bias the diffusion model and repetition‐based methods .…”

Section: Introductionmentioning

confidence: 99%

Diffusion MRI noise mapping using random matrix theory

Veraart

Fieremans

Novikov

2015

Magnetic Resonance in Med

622

496

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Purpose To estimate the spatially varying noise map using a redundant magnitude MR series. Methods We exploit redundancy in non-Gaussian multi-directional diffusion MRI data by identifying its noise-only principal components, based on the theory of noisy covariance matrices. The bulk of PCA eigenvalues, arising due to noise, is described by the universal Marchenko-Pastur distribution, parameterized by the noise level. This allows us to estimate noise level in a local neighborhood based on the singular value decomposition of a matrix combining neighborhood voxels and diffusion directions. Results We present a model-independent local noise mapping method capable of estimating noise level down to about 1% error. In contrast to current state-of-the art techniques, the resultant noise maps do not show artifactual anatomical features that often reflect physiological noise, the presence of sharp edges, or a lack of adequate a priori knowledge of the expected form of MR signal. Conclusions Simulations and experiments show that typical diffusion MRI data exhibit sufficient redundancy that enables accurate, precise, and robust estimation of the local noise level by interpreting the PCA eigenspectrum in terms of the Marchenko-Pastur distribution.

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Local estimation of the noise level in MRI using structural adaptation

Cited by 21 publications

References 40 publications

Spinal cord atrophy in a primary progressive multiple sclerosis trial: Improved sample size using GBSI

Spinal cord atrophy in a primary progressive multiple sclerosis trial: Improved sample size using GBSI

Image denoising substantially improves accuracy and precision of intravoxel incoherent motion parameter estimates

Diffusion MRI noise mapping using random matrix theory

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