Distinct patterns of active and non-active plaques using texture analysis on brain NMR images in multiple sclerosis patients: preliminary results

Yu, Olivier; Mauss, Y.; Zöllner, G.; Namer, Izzie Jacques; Chambron, J.

doi:10.1016/s0730-725x(99)00062-4

Cited by 66 publications

(52 citation statements)

References 18 publications

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“…50,51 Texture analysis has been proposed as an alternative strategy for identifying active MS lesions and monitoring disease progression. For example, Yu et al 52 discovered that texture analysis of standard T2-weighted MR images could discriminate between active and nonactive lesions in a study of 8 patients with RRMS (4 with active lesions), suggesting that this technique could be used to minimize or perhaps even obviate gadolinium-based contrast. Specifically, the authors evaluated 42 first-and second-order statistical textural features and performed LDA to classify lesions into active and nonactive groups.…”

Section: Msmentioning

confidence: 99%

Texture Analysis: A Review of Neurologic MR Imaging Applications

Kassner¹,

Thornhill²

2010

AJNR Am J Neuroradiol

352

265

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SUMMARY:Texture analysis describes a variety of image-analysis techniques that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Texture analysis may be particularly well-suited for lesion segmentation and characterization and for the longitudinal monitoring of disease or recovery. We begin this review by outlining the general procedure for performing texture analysis, identifying some potential pitfalls and strategies for avoiding them. We then provide an overview of some intriguing neuro-MR imaging applications of texture analysis, particularly in the characterization of brain tumors, prediction of seizures in epilepsy, and a host of applications to MS.ABBREVIATIONS: ABSV ϭ absolute gradient value; AIS ϭ acute ischemic stroke; ANN ϭ artificial neural network; CIS ϭ clinically isolated syndrome; d ϭ distance; DCE ϭ dynamic contrastenhanced; FCD ϭ focal cortical dysplasia; FLAIR ϭ fluid-attenuated inversion recovery; f x ϭ second-order gray-level co-occurrence feature number "x"; GLCM ϭ gray-level co-occurrence matrix; GM ϭ gray matter; HT ϭ hemorrhagic transformation; LDA ϭ linear discriminant analysis; MGL ϭ mean gray level; MGR ϭ mean gradient; MS ϭ multiple sclerosis; MTR ϭ magnetization transfer ratio; NAWM ϭ normal-appearing white matter; N g ϭ number of gray levels; PCA ϭ principal components analysis; PPMS ϭ primary-progressive MS; RLM ϭ run-length matrix; ROC ϭ receiver-operator characteristic; ROI ϭ region of interest; RRMS ϭ relapsing-remitting MS; rtPA ϭ recombinant tissue plasminogen activator; SPMS ϭ secondary-progressive MS; SVM ϭ support vector machine; ϭ direction; VGL ϭ variance of gray levels; VGR ϭ variance gradient; WM ϭ white matter

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Section: Msmentioning

confidence: 99%

Texture Analysis: A Review of Neurologic MR Imaging Applications

Kassner¹,

Thornhill²

2010

AJNR Am J Neuroradiol

352

265

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“…Texture analysis generates computer measures that quantitatively describe texture content in an image. A majority of published accounts have used statistical techniques, the co-occurrence matrix method 5 for instance, to analyze image texture in MS [6][7][8] , and other diseases. 9 In particular, statistical texture analysis has been applied with promise to: distinguish active and inactive lesions; differentiate normal and pathological spinal cord; and, characterize therapeutic response in T2-weighted MRI in MS.…”

Section: Introductionmentioning

confidence: 99%

A Novel MRI Texture Analysis of Demyelination and Inflammation in Relapsing-Remitting Experimental Allergic Encephalomyelitis

Zhang

Wells

Buist

et al. 2006

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006

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Abstract.We have developed a novel multiscale localized image texture analysis technique, based upon the polar Stockwell Transform (PST). In this paper we characterized image texture in vivo using the PST in histologically verified lesion areas in T2-weighted MRI of an animal model of multiple sclerosis. Both high and low frequency signals, representing inflammation and demyelination, were significantly increased in pathological regions compared to normal control tissue. This suggests that this new local spatial-frequency measure of image texture may provide a sensitive and precise indication of disease activity.

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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 .…”

Section: Zhang Et Almentioning

confidence: 99%

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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Distinct patterns of active and non-active plaques using texture analysis on brain NMR images in multiple sclerosis patients: preliminary results

Cited by 66 publications

References 18 publications

Texture Analysis: A Review of Neurologic MR Imaging Applications

Texture Analysis: A Review of Neurologic MR Imaging Applications

A Novel MRI Texture Analysis of Demyelination and Inflammation in Relapsing-Remitting Experimental Allergic Encephalomyelitis

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

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