Adaptive non‐local means denoising of MR images with spatially varying noise levels

Manjón, José V.; Coupé, Pierrick; Martí‐Bonmatí, Luis; Collins, D. Louis; Robles, Montserrat

doi:10.1002/jmri.22003

Cited by 866 publications

(640 citation statements)

References 25 publications

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“…The realigned, bias corrected images were then tissue‐classified into GM, WM, and cerebrospinal fluid (CSF) and registered to Montreal Neurological Institute (MNI) space through linear and nonlinear transformations (Ashburner, 2007; Kurth et al., 2014; Luders et al., 2009) (see http://dbm.neuro.uni-jena.de/vbm8/VBM8-Manual.pdf). More specifically, the tissue classification was based on maximum a posteriori segmentations (Rajapakse, Giedd, & Rapoport, 1997), accounted for partial volume effects (Tohka, Zijdenbos, & Evans, 2004), and was refined by applying a spatially adaptive nonlocal means denoising filter (Manjon, Coupe, Marti‐Bonmati, Collins, & Robles, 2010) as well as a hidden Markov random field model (Cuadra, Cammoun, Butz, Cuisenaire, & Thiran, 2005). These methods made the tissue classification independent of tissue probability maps and thus additionally minimized the influence of misclassifications, lesions, and altered geometry (Ceccarelli et al., 2012).…”

Section: Methodsmentioning

confidence: 99%

Estriol‐mediated neuroprotection in multiple sclerosis localized by voxel‐based morphometry

et al. 2018

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IntroductionProgressive gray matter (GM) atrophy is a hallmark of multiple sclerosis (MS). Cognitive impairment has been observed in 40%–70% of MS patients and has been linked to GM atrophy. In a phase 2 trial of estriol treatment in women with relapsing–remitting MS (RRMS), higher estriol levels correlated with greater improvement on the paced auditory serial addition test (PASAT) and imaging revealed sparing of localized GM in estriol‐treated compared to placebo‐treated patients. To better understand the significance of this GM sparing, the current study explored the relationships between the GM sparing and traditional MRI measures and clinical outcomes.MethodsSixty‐two estriol‐ and forty‐nine placebo‐treated RRMS patients underwent clinical evaluations and brain MRI. Voxel‐based morphometry (VBM) was used to evaluate voxelwise GM sparing from high‐resolution T1‐weighted scans.ResultsA region of treatment‐induced sparing (TIS) was defined as the areas where GM was spared in estriol‐ as compared to placebo‐treated groups, localized primarily within the frontal and parietal cortices. We observed that TIS volume was directly correlated with improvement on the PASAT. Next, a longitudinal cognitive disability‐specific atlas (DSA) was defined by correlating voxelwise GM volumes with PASAT scores, that is, areas where less GM correlated with less improvement in PASAT scores. Finally, overlap between the TIS and the longitudinal cognitive DSA revealed a specific region of cortical GM that was preserved in estriol‐treated subjects that was associated with better performance on the PASAT.ConclusionsDiscovery of this region of overlap was biology driven, not based on an a priori structure of interest. It included the medial frontal cortex, an area previously implicated in problem solving and attention. These findings indicate that localized GM sparing during estriol treatment was associated with improvement in cognitive testing, suggesting a clinically relevant, disability‐specific biomarker for clinical trials of candidate neuroprotective treatments in MS.

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Section: Methodsmentioning

confidence: 99%

Estriol‐mediated neuroprotection in multiple sclerosis localized by voxel‐based morphometry

et al. 2018

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“…During normalization, images were interpolated to isotropic 1 3 1 3 1 mm voxels. The VBM8-toolbox extends this model with a partial volume estimation to account for partial volume effects and the application of a spatially adaptive non-local means (SANLM) filter 47 for bias correction. Normalization to stereotactic space consisted of a linear affine registration and a linear deformation corresponding to a high-dimensional DARTEL normalization 48 implemented in VBM8.…”

Section: Article Researchmentioning

confidence: 99%

CNVs conferring risk of autism or schizophrenia affect cognition in controls

Stefánsson

Meyer‐Lindenberg

Steinberg

et al. 2013

Nature

602

554

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In a small fraction of patients with schizophrenia or autism, alleles of copy-number variants (CNVs) in their genomes are probably the strongest factors contributing to the pathogenesis of the disease. These CNVs may provide an entry point for investigations into the mechanisms of brain function and dysfunction alike. They are not fully penetrant and offer an opportunity to study their effects separate from that of manifest disease. Here we show in an Icelandic sample that a few of the CNVs clearly alter fecundity (measured as the number of children by age 45). Furthermore, we use various tests of cognitive function to demonstrate that control subjects carrying the CNVs perform at a level that is between that of schizophrenia patients and population controls. The CNVs do not all affect the same cognitive domains, hence the cognitive deficits that drive or accompany the pathogenesis vary from one CNV to another. Controls carrying the chromosome 15q11.2 deletion between breakpoints 1 and 2 (15q11.2(BP1-BP2) deletion) have a history of dyslexia and dyscalculia, even after adjusting for IQ in the analysis, and the CNVonly confers modest effects on other cognitive traits. The 15q11.2(BP1-BP2) deletion affects brain structure in a pattern consistent with both that observed during first-episode psychosis in schizophrenia and that of structural correlates in dyslexia.Little information is available on whether or how rare CNVs conferring high risk of schizophrenia and/or autism affect physiologic function of otherwise normal brains. As none of these CNVs hitherto described are fully penetrant for the diseases, and both schizophrenia and autism affect cognition, we aimed to examine the possibility that the CNVs affect cognition in control carriers, those who do not suffer either disease or intellectual disability. We based our selection of CNVs on a literature search for CNVs associated with schizophrenia and/or autism ('neuropsychiatric CNVs'); this search produced 26 CNV alleles (Supplementary Table 1) 1-3 . These CNV alleles are rare, found in 0.002% to 0.2% frequency, and cumulatively in 1.16% of our sample of 101,655 genotyped subjects, representing approximately one-third of the Icelandic population ( Supplementary Tables 1 and 2).We used the subset of genotyped subjects born before 1968, without excluding patients, to examine the association of each neuropsychiatric CNV with reproductive outcome ('fecundity'), defined simply as the number of children each subject had by age 45. After correction for multiple comparisons, three neuropsychiatric CNVs were significantly associated with fecundity (Table 1). Subjects carrying the 16p11.2 deletion or the 22q11.21 duplication show reduced fecundity, with the effect in males significantly greater than in females (P 5 0.0083 and P 5 0.029 for the difference in effect by sex for the 16p11.2 deletion and the 22q11.21 duplication, respectively). In contrast, individuals carrying the 16p12.1 deletion have more children than do controls (Table 1). Those with deletions at 15q11.2(...

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“…One approach that has been successfully used in the past for correcting the noise underestimation at low SNR areas is based on the iterative analytical correction scheme proposed by Koay and Basser (2006). This approach was used for stationary noise estimation in MRI (Coupe et al, 2010;Manjón et al, 2010).…”

Section: Rician Noise Estimationmentioning

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%

MRI noise estimation and denoising using non-local PCA

Manjón

Coupé²,

Buades³

2015

Medical Image Analysis

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115

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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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Adaptive non‐local means denoising of MR images with spatially varying noise levels

Cited by 866 publications

References 25 publications

Estriol‐mediated neuroprotection in multiple sclerosis localized by voxel‐based morphometry

Estriol‐mediated neuroprotection in multiple sclerosis localized by voxel‐based morphometry

CNVs conferring risk of autism or schizophrenia affect cognition in controls

MRI noise estimation and denoising using non-local PCA

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