Evaluation of Non-Local Means Based Denoising Filters for Diffusion Kurtosis Imaging Using a New Phantom

Zhou, Minxiong; Xu, Yan; Xie, Honglan; Zheng, Hui; Xu, Dongrong; Yang, Guang

doi:10.1371/journal.pone.0116986

Cited by 17 publications

(19 citation statements)

References 47 publications

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“…Due to the lack of a gold standard, accuracy of the IVIM parameter estimates could not be determined in vivo. To investigate transferability of the results of the in-silico simulations to the in-vivo situation, a ground truth was generated from the in-vivo measurements in a similar manner as proposed by Zhou et al [41]. The IVIM parameters in the resulting in-vivo ground truth agreed well with literature values (see Tables 1 and 2) [31, 42].…”

Section: Discussionmentioning

confidence: 84%

“…For comparison with the in-silico simulations, a ground truth was generated from the in-vivo data using a similar approach as proposed by Zhou et al [41]. In the process, a) the DW image series were eddy-current corrected, motion compensated and coregistered, b) the DW image series were denoised using the JREC algorithm and subsequently averaged, and c) the IVIM parameter maps were calculated.…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 84%

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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“…For both the NR-NLM and NR-MS-NLM filters, h was set to the true input value of σ , as was found to be optimal in previous studies [1], [5]–[6], [24]–[27]. For both the NR-NLML and NR-MS-NLML filters, M was fixed to 50 and σ was set to the true input value.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, Wiest-Daesslé et al [26] extended the NLM filter to the multispectral case of diffusion tensor imaging of the brain, and found that the use of information acquired from different diffusion gradient directions improved upon classical denoising methods. In a more recent application of multispectral filtering, Zhou et al [27] found that the use of redundant information acquired with different b -values produced a reliable estimation of diffusion kurtosis in human brain.…”

Section: Introductionmentioning

confidence: 99%

Noise Estimation and Reduction in Magnetic Resonance Imaging Using a New Multispectral Nonlocal Maximum-likelihood Filter

Bouhrara

Bonny²,

Ashinsky

et al. 2017

IEEE Trans. Med. Imaging

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Denoising of magnetic resonance (MR) images enhances diagnostic accuracy, the quality of image manipulations such as registration and segmentation, and parameter estimation. The first objective of this paper is to introduce a new, high-performance, nonlocal filter for noise reduction in MR image sets consisting of progressively-weighted, that is, multispectral, images. This filter is a multispectral extension of the nonlocal maximum likelihood filter (NLML) combining both spatial and spectral information, that is, signal intensities across multiple image weightings, to perform efficient denoising. Performance was evaluated on synthetic and in-vivo T2- and T1-weighted brain imaging data, and compared to the nonlocal-means (NLM) and its multispectral version, that is, MS-NLM, and the nonlocal maximum likelihood (NLML) filters. Visual inspection of filtered images and quantitative analyses showed that all filters provided substantial reduction of noise. Further, as expected, the use of multispectral information improves filtering quality. In addition, numerical and experimental analyses indicated that the new multispectral NLML filter, MS-NLML, demonstrated markedly less blurring and loss of image detail than seen with the other filters evaluated. In addition, since noise standard deviation (SD) is an important parameter for all of those nonlocal filters, a multispectral extension of the method of maximum likelihood estimation (MLE) of noise amplitude is presented and compared to both local and nonlocal MLE methods. Numerical and experimental analyses indicated the superior performance of this multispectral method for estimation of noise SD.

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“…To date, various denoising methods have been developed to improve the quality of DW images, such as the Gaussian lter (30,31), anisotropic diffusion lter (32)(33)(34), linear minimum mean squared error lter (21), random matrix theory (35), multi-shell position-orientation-adaptive smoothing (36) and nonlocal means (NLM) lter (37,38). In particular, NLM lter has been suggested to signi cantly improve MR data quality by reducing Rician noise (20,(39)(40)(41) and implemented to DTI (42)(43)(44) and DKI (45) in the human brain. Moreover, NLM lter has shown to provide e ciency of noise removal while the ne structures and details of images are well preserved (40,(46)(47)(48)(49)(50)(51).…”

Section: Introductionmentioning

confidence: 99%

Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord

Zhang

Vernekar

Qian

et al. 2020

Preprint

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Background To investigate the effect of using an Rician nonlocal means (NLM) filter on quantification of diffusion tensor (DT)- and diffusion kurtosis (DK)-derived metrics in various anatomical regions of the human brain and the spinal cord, when combined with a constrained linear least squares (CLLS) approach. Methods Prospective brain data from 9 healthy subjects and retrospective spinal cord data from 5 healthy subjects from a 3T MRI scanner were included in the study. Prior to tensor estimation, registered diffusion weighted images were denoised by an optimized blockwise NLM filter with CLLS. Mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA), were determined in anatomical structures of the brain and the spinal cord. DTI and DKI metrics, signal-to-noise ratio (SNR) and Chi-square values were quantified in distinct anatomical regions for all subjects, with and without Rician denoising. Results The averaged SNR significantly increased with Rician denoising by a factor of 2 while the averaged Chi-square values significantly decreased up to 61% in the brain and up to 43% in the spinal cord after Rician NLM filtering. In the brain, the mean MK varied from 0.70 (putamen) to 1.27 (internal capsule) while AK and RK varied from 0.58 (corpus callosum) to 0.92 (cingulum) and from 0.70 (putamen) to 1.98 (corpus callosum), respectively. In the spinal cord, FA varied from 0.78 in lateral column to 0.81 in dorsal column while MD varied from 0.91 × 10− 3 mm2/s (lateral) to 0.93 × 10− 3 mm2/s (dorsal). RD varied from 0.34 × 10− 3 mm2/s (dorsal) to 0.38 × 10− 3 mm2/s (lateral) and AD varied from 1.96 × 10− 3 mm2/s (lateral) to 2.11 × 10− 3 mm2/s (dorsal). Conclusions Our results show Rician denoising NLM filter incorporated with CLLS significantly increases SNR and reduces estimation errors of DT- and KT-derived metrics, providing the reliable metrics estimation with adequate SNR levels.

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Evaluation of Non-Local Means Based Denoising Filters for Diffusion Kurtosis Imaging Using a New Phantom

Cited by 17 publications

References 47 publications

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

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

Noise Estimation and Reduction in Magnetic Resonance Imaging Using a New Multispectral Nonlocal Maximum-likelihood Filter

Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord

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