New methods for MRI denoising based on sparseness and self-similarity

Manjón, José V.; Coupé, Pierrick; Buades, Antoni; Collins, D. Louis; Robles, Montserrat

doi:10.1016/j.media.2011.04.003

Cited by 248 publications

(167 citation statements)

References 29 publications

(38 reference statements)

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“…As demonstrated in Manjón et al (2012), when a good quality pre-filtered image is available we can use this image to guide the similarity estimation process of a rotationally invariant version of the NLM filter.…”

Section: Rotational Invariant Non-local Pca Denoisingmentioning

confidence: 99%

“…In MRI, early works using the NLM method are from Coupe et al (2008) and Manjón et al (2008). The bibliography related to this method is quite extensive (Tristan-Vega et al, 20012;Coupe et al, 2012;Manjón et al, 2009Manjón et al, , 2010Manjón et al, , 2012Wiest-Daesslé et al, 2008;He and Greenshields, 2009;Rajan et al 2012Rajan et al , 2014.…”

Section: Introductionmentioning

confidence: 99%

“…The resulting filtered image is used as a guide image to accurately estimate the voxel similarities within a rotationally invariant NLM (PRI-NLM) strategy as done in Manjón et al (2012). The increased quality of this guide image resulting from the proposed PCA-based noise removal method significantly improves the application of the PRI-NLM filter boosting the overall denoising performance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MRI noise estimation and denoising using non-local PCA

Manjón

Coupé²,

Buades³

2015

Medical Image Analysis

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152

122

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This paper proposes a novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images. The proposed method is a two-stage approach that first filters the noisy image using a non local PCA thresholding strategy by automatically estimating the local noise level present in the image and second uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed method internally estimates the amount of local noise presents in the images that enables applying it automatically to images with spatially varying noise levels and also corrects the Rician noise induced bias locally. The proposed approach has been compared with related state-of-the-art methods showing competitive results in all the studied cases.3

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Section: Rotational Invariant Non-local Pca Denoisingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MRI noise estimation and denoising using non-local PCA

Manjón

Coupé²,

Buades³

2015

Medical Image Analysis

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152

122

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“…Since anisotropic voxels were previously shown to be suboptimal for fiber tractography [20], we resampled each volume to 2 mm isotropic resolution prior to any analysis. We also applied a Rician-adapted denoising filter [21] to eliminate nonstationary noise commonly observed in DW images, since our acquisition model described in Section 2.1 assumes Gaussian noise. We then warped our functionally derived group parcellation map to the b=0 volume of each subject using FSL [22] to facilitate the computation of fiber count.…”

Section: Dmri Datamentioning

confidence: 99%

Dictionary Based Super-Resolution for Diffusion MRI

Yoldemir

Bajammal

Abugharbieh

2014

Computational Diffusion MRI

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Diffusion magnetic resonance imaging (dMRI) provides unique capabilities for non-invasive mapping of fiber tracts in the brain. It however suffers from relatively low spatial resolution, often leading to partial volume effects. In this paper, we propose to use a super-resolution approach based on dictionary learning for alleviating this problem. Unlike the majority of existing super-resolution algorithms, our proposed solution does not entail acquiring multiple scans from the same subject which renders it practical in clinical settings and applicable to legacy data. Moreover, this approach can be used in conjunction with any diffusion model. Motivated by how functional connectivity (FC) reflects the underlying structural connectivity (SC), we quantitatively validate our results by investigating the consistency between SC and FC before and after super-resolving the data. Based on this scheme, we show that our method outperforms traditional interpolation strategies and the only existing single image super-resolution method for dMRI that is not dependent on a specific diffusion model. Qualitatively, we illustrate that fiber tracts and tract-density maps reconstructed from super-resolved dMRI data reveal exquisite details beyond what is achievable with the original data.

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“…This method does not require the prior knowledge of the coil sensitivity profiles in the MRI scanner. Manjon et al [19] proposed the denoising methods for three dimensional MR images by exploiting the sparseness and self-similarity properties of the images. These methods are based on a three dimensional moving window cosine transform hard thresholding and a three dimensional rotationally invariant version of the NLM filter.…”

Section: Introductionmentioning

confidence: 99%

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

Mohan¹,

Krishnaveni²,

Guo³

2013

Measurement Science Review

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In this paper, a new filtering method based on neutrosophic set (NS) approach of wiener filter is presented to remove Rician noise from magnetic resonance image. A neutrosophic set, a part of neutrosophy theory, studies the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. The image is transformed into NS domain, described using three membership sets: True (T), Indeterminacy (I) and False (F). The entropy of the neutrosophic set is defined and employed to measure the indeterminacy. The ω-wiener filtering operation is used on T and F to decrease the set indeterminacy and remove noise. The experiments have conducted on simulated Magnetic Resonance images (MRI) from Brainweb database and clinical MR images corrupted by Rician noise. The results show that the NS wiener filter produces better denoising results in terms of visual perception, qualitative and quantitative measures compared with other denoising methods, such as classical wiener filter, the anisotropic diffusion filter, the total variation minimization scheme and non local means filter.

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New methods for MRI denoising based on sparseness and self-similarity

Cited by 248 publications

References 29 publications

MRI noise estimation and denoising using non-local PCA

MRI noise estimation and denoising using non-local PCA

Dictionary Based Super-Resolution for Diffusion MRI

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

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