Pathological correlates of magnetic resonance imaging texture heterogeneity in multiple sclerosis

Zhang, Yunyan; Moore, George R.; Laule, Cornelia; Bjarnason, Thorarin A.; Kozłowski, Piotr; Traboulsee, Anthony; Li, David K.B.

doi:10.1002/ana.23867

Cited by 57 publications

(62 citation statements)

References 37 publications

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“…In vivo mapping of myelin density15 and g‐ratio16; iron deposition17; sodium concentration18; inflammation and axonal injury19 are examples of quantitative MRI techniques being developed to detect early multiple sclerosis pathology. Similar quantitative MRI metrics require extensive validation, in order to confirm their specificity and relevance to clinical outcomes 20, 21, 22…”

Section: Introductionmentioning

confidence: 99%

Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?

Grussu

Schneider

Tur

et al. 2017

Ann Clin Transl Neurol

265

243

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ObjectiveConventional magnetic resonance imaging (MRI) of the multiple sclerosis spinal cord is limited by low specificity regarding the underlying pathological processes, and new MRI metrics assessing microscopic damage are required. We aim to show for the first time that neurite orientation dispersion (i.e., variability in axon/dendrite orientations) is a new biomarker that uncovers previously undetected layers of complexity of multiple sclerosis spinal cord pathology. Also, we validate against histology a clinically viable MRI technique for dispersion measurement (neurite orientation dispersion and density imaging, NODDI), to demonstrate the strong potential of the new marker.MethodsWe related quantitative metrics from histology and MRI in four post mortem spinal cord specimens (two controls; two progressive multiple sclerosis cases). The samples were scanned at high field, obtaining maps of neurite density and orientation dispersion from NODDI and routine diffusion tensor imaging (DTI) indices. Histological procedures provided markers of astrocyte, microglia, myelin and neurofilament density, as well as neurite dispersion.ResultsWe report from both NODDI and histology a trend toward lower neurite dispersion in demyelinated lesions, indicative of reduced neurite architecture complexity. Also, we provide unequivocal evidence that NODDI‐derived dispersion matches its histological counterpart (P < 0.001), while DTI metrics are less specific and influenced by several biophysical substrates.InterpretationNeurite orientation dispersion detects a previously undescribed and potentially relevant layer of microstructural complexity of multiple sclerosis spinal cord pathology. Clinically feasible techniques such as NODDI may play a key role in clinical trial and practice settings, as they provide histologically meaningful dispersion indices.

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Section: Introductionmentioning

confidence: 99%

Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?

Grussu

Schneider

Tur

et al. 2017

Ann Clin Transl Neurol

265

243

View full text Add to dashboard Cite

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“…Although, to the best of our knowledge, there are no studies correlating in vivo texture measures with histopathology, Zhang et al 31 performed a study in which they found correlation among histopathology and texture parameters obtained from MR imaging of postmortem tissue samples of patients with multiple sclerosis. Possible tissue changes in the medulla oblongata are in line with previous pathologic reports in FA.…”

Section: Discussionmentioning

confidence: 99%

MRI Texture Analysis Reveals Bulbar Abnormalities in Friedreich Ataxia

Santos

Maistro

Silva

et al. 2015

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE:Texture analysis is an image processing technique that can be used to extract parameters able to describe meaningful features of an image or ROI. Texture analysis based on the gray level co-occurrence matrix gives a second-order statistical description of the image or ROI. In this work, the co-occurrence matrix texture approach was used to extract information from brain MR images of patients with Friedreich ataxia and a control group, to see whether texture parameters were different between these groups. A longitudinal analysis was also performed.

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“…Using standard MR images, texture analysis demonstrates the ability to differentiate normal from abnormal MS tissue, and to distinguish active from inactive MS lesions in the brain [9][10][11] . Recently, using a new local spatial frequency-based algorithm [12][13][14] , MRI texture analysis detected differences of demyelination, axonal injury and inflammation between focal and diffuse MS abnormalities in fixed postmortem brain samples 15 . In the current study, we adapted this localized texture analysis method to determine whether different types of MS lesions…”

Section: Zhang Et Almentioning

confidence: 99%

“…The signal intensity patterns of T2-weighted MRI were evaluated using a local spatial frequency-based program as reported previously 15 . In brief, the entire frequency content enclosed in the whole MR image was initially calculated using the classical Fourier transform; then, the spatial distribution of each frequency at individual spatial resolutions of the image was determined by applying the inverse Fourier transform.…”

Section: Mri Texture Analysismentioning

confidence: 99%

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Multi-scale MRI spectrum detects differences in myelin integrity between MS lesion types

et al. 2016

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Running head: T2 MRI spectra of myelin in MS lesions Number of characters in the title = 84 (with spaces); in the running head = 38; Number of words in the abstract = 241; in the body of manuscript = 3097 Number of figures = 4; color figures = 1; tables = 1. Zhang et al 2 AbstractObjective: Lesions with different types of pathology are found at autopsy in multiple sclerosis (MS). The goal of this study was to determine lesion characteristic in MRI using clinical protocols.Methods: We imaged fresh brain specimens from 21 MS patients using 1.5T scanners and applied image texture analysis. Through staining of tissue sections for inflammation (microglia and microphages) and myelin (proteolipid protein), MS lesions were classified into 5 categories: pre-active, active, chronic active, chronic inactive, and remyelinated.Based on standard T2-weighted images, texture analysis was performed using a local spatial frequency-based approach that provided multi-scale MR spectra at each voxel.Results: Following linear mixed-effect modeling, we found that normalized MRI texture heterogeneity was greater in active, chronic active, and chronic inactive lesions that had complete demyelination than in normal appearing white matter (NAWM; p < 0.01). In combination, these 3 lesion types as a group were more heterogeneous than pre-active (p = 0.02) lesions and NAWM (p < 0.01), both with apparently intact myelin. Furthermore, the distribution of remyelinated spectra was similar to that of NAWM but different from demyelinated lesions (p < 0.01).Interpretation: While the 5 defined tissue categories not completely distinguishable at 1.5T, we differentiated demyelination from remyelination and discovered that changes in myelin content drove the severity of texture abnormality. Assessing local patterns of image spectra in clinical MRI may become a useful approach in determining the efficacy of remyelination strategies in MS and other demyelinating disorders (241).Zhang et al 3

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Pathological correlates of magnetic resonance imaging texture heterogeneity in multiple sclerosis

Abstract: Texture analysis performed on routine clinical magnetic resonance images may be a potential measure of tissue integrity. Tissues with more severe myelin and axonal pathology are associated with greater texture heterogeneity.

Cited by 57 publications

References 37 publications

Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?

Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?

MRI Texture Analysis Reveals Bulbar Abnormalities in Friedreich Ataxia

Multi-scale MRI spectrum detects differences in myelin integrity between MS lesion types

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