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Pathological Correlates Of Magnetic Resonance Imaging Texture Heterogeneity In Multiple Sclerosis

Y. Zhang, G. R. Wayne Moore, C. Laule, Thorarin A. Bjarnason, P. Kozlowski, A. Traboulsee, David K. B. Li
Published 2013 · Medicine

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To analyze the texture of T2‐weighted magnetic resonance imaging (MRI) of postmortem multiple sclerosis (MS) brain, and to determine whether and how MRI texture correlates with tissue pathology.
This paper references
10.1016/J.CRAD.2004.07.008
Texture analysis of medical images.
G. Castellano (2004)
MR Multi-Spectral Texture Analysis Using Space-Frequency Information
Hongmei Zhu (2004)
10.1109/78.492555
Localization of the complex spectrum: the S transform
R. G. Stockwell (1996)
10.1016/j.clineuro.2010.03.022
Role of MRI in diagnosis and treatment of multiple sclerosis
M. A. Sahraian (2010)
Prospects for early detection of Alzheimer's disease from serial MR images in transgenic mice.
M. Muskulus (2009)
Multiscale amplitudemodulation frequency-modulation (AM-FM) texture analysis of multiple sclerosis in brain MRI images
CP Loizou (2010)
10.1016/j.acra.2010.01.005
MRI texture analysis in multiple sclerosis: toward a clinical analysis protocol.
Lara Harrison (2010)
10.1038/nrn2480
Remyelination in the CNS: from biology to therapy
R. Franklin (2008)
10.1212/WNL.45.2.255
Correlations between changes in disability and T2‐weighted brain MRI activity in multiple sclerosis
M. Filippi (1995)
10.1158/1078-0432.CCR-07-1964
The Use of Magnetic Resonance Imaging to Noninvasively Detect Genetic Signatures in Oligodendroglioma
R. Brown (2008)
10.1177/1352458506070928
Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology
C. Laule (2006)
10.1016/j.neuroimage.2007.12.008
Myelin water imaging of multiple sclerosis at 7 T: Correlations with histopathology
C. Laule (2008)
10.1016/j.neuroimage.2009.09.049
An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging
S. Drabycz (2010)
Normal-appearing white matter changes vary with distance to lesions in multiple sclerosis.
H. Vrenken (2006)
10.1001/archneurol.2009.57
Diffusely abnormal white matter in chronic multiple sclerosis: imaging and histopathologic analysis.
A. Seewann (2009)
10.1016/S0730-725X(99)00062-4
Distinct patterns of active and non-active plaques using texture analysis on brain NMR images in multiple sclerosis patients: preliminary results.
O. Yu (1999)
10.1002/ana.20832
Magnetic resonance imaging as a surrogate outcome measure of disability in multiple sclerosis: Have we been overly harsh in our assessment?
D. Goodin (2006)
10.1007/s00281-009-0182-3
Progressive multiple sclerosis
M. Bradl (2009)
10.1002/ana.22521
Multiple sclerosis normal‐appearing white matter: Pathology–imaging correlations
N. Moll (2011)
10.1007/11866565_93
A Novel MRI Texture Analysis of Demyelination and Inflammation in Relapsing-Remitting Experimental Allergic Encephalomyelitis
Y. Zhang (2006)
10.1177/1352458510395981
Texture analysis differentiates persistent and transient T1 black holes at acute onset in multiple sclerosis: A preliminary study
Y. Zhang (2011)
10.1111/j.1552-6569.2007.00131.x
T1‐ and T2‐Based MRI Measures of Diffuse Gray Matter and White Matter Damage in Patients with Multiple Sclerosis
M. Neema (2007)
10.1002/jmri.21885
Texture analysis of magnetization transfer maps from patients with clinically isolated syndrome and multiple sclerosis
D. Tozer (2009)
10.1016/j.mri.2008.07.014
Pattern recognition system for the discrimination of multiple sclerosis from cerebral microangiopathy lesions based on texture analysis of magnetic resonance images.
P. Theocharakis (2009)
10.1177/1352458510384008
Pathological basis of diffusely abnormal white matter: insights from magnetic resonance imaging and histology
C. Laule (2011)
10.1016/j.neuroimage.2009.03.075
T2 MRI texture analysis is a sensitive measure of tissue injury and recovery resulting from acute inflammatory lesions in multiple sclerosis
Y. Zhang (2009)
10.1007/s00330-009-1605-1
Three-dimensional textural analysis of brain images reveals distributed grey-matter abnormalities in schizophrenia
B. Ganeshan (2009)
10.1016/0022-510X(87)90184-5
Formalin fixed brains are useful for magnetic resonance imaging (MRI) study
H. Nagara (1987)
10.1016/j.mri.2008.01.016
Texture analysis of multiple sclerosis: a comparative study.
J. Zhang (2008)
10.1002/mrm.22147
Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme
E. I. Zacharaki (2009)
10.1109/TITB.2010.2091279
Multiscale Amplitude-Modulation Frequency-Modulation (AM–FM) Texture Analysis of Multiple Sclerosis in Brain MRI Images
Christos P. Loizou (2011)
10.2174/156720509790147089
Prospects for Early Detection of Alzheimers Disease from Serial MR Images in Transgenic Mouse Models
M. Muskulus (2009)
10.1002/(SICI)1522-2594(199911)42:5<929::AID-MRM13>3.0.CO;2-2
Texture analysis of spinal cord pathology in multiple sclerosis
J. M. Mathias (1999)
10.1016/j.acra.2009.08.012
Characterization of breast cancer types by texture analysis of magnetic resonance images.
K. Holli (2010)
10.1002/ana.20202
Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain
K. Schmierer (2004)
10.1118/1.3081408
Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented study.
M. Mayerhoefer (2009)
10.1002/ana.21113
Imaging correlates of axonal swelling in chronic multiple sclerosis brains
E. Fisher (2007)
10.1016/J.MRI.2004.08.017
Remyelination assessment by MRI texture analysis in a cuprizone mouse model.
O. Yu (2004)
An in vivo and post mortem MRI study in multiple sclerosis with pathological correlation.
G. Macćhi (1992)
10.1002/jmri.22095
Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft‐tissue tumors in T1‐MRI images
Jaber Juntu (2010)
10.1002/mrm.21909
Postmortem MRI of human brain hemispheres: T2 relaxation times during formaldehyde fixation
R. Dawe (2009)



This paper is referenced by
10.3389/fnagi.2018.00393
Predicting the Development of Normal-Appearing White Matter With Radiomics in the Aging Brain: A Longitudinal Clinical Study
Y. Shao (2018)
Serial MRI Analysis to Track Long-term Evolution of White Matter Lesions in Multiple Sclerosis
Yufan Zheng (2017)
10.1093/ons/opz288
Magnetic Resonance-Based Radiomic Analysis of Radiofrequency Lesion Predicts Outcomes After Percutaneous Cordotomy: A Feasibility Study.
A. Vedantam (2019)
10.3174/ajnr.A4455
MRI Texture Analysis Reveals Bulbar Abnormalities in Friedreich Ataxia
T. A. Santos (2015)
10.1002/acn3.655
Evaluating the cerebral correlates of survival in amyotrophic lateral sclerosis
Abdullah Ishaque (2018)
10.1212/WNL.0000000000002106
Beyond focal cortical lesions in MS
C. Louapre (2015)
10.1517/21678707.2014.925393
New and emerging treatments of Guillain–Barré syndrome
H. C. Lehmann (2014)
10.1038/srep32647
The Neuromelanin-related T2* Contrast in Postmortem Human Substantia Nigra with 7T MRI
J. Lee (2016)
10.14735/AMCSNN2017700
Quantitative MRI Texture Analysis in Differentiating Enhancing and Non-enhancing T1-hypointense Lesions without Application of Contrast Agent in Multiple Sclerosis
A. A. Ardakani (2017)
10.1503/jpn.180171
Magnetic resonance imaging texture predicts progression to dementia due to Alzheimer disease earlier than hippocampal volume
S. Lee (2019)
10.1016/j.nicl.2014.01.003
MRI texture heterogeneity in the optic nerve predicts visual recovery after acute optic neuritis☆
Y. Zhang (2014)
10.1186/s42492-019-0025-6
Robustness of radiomic features in magnetic resonance imaging: review and a phantom study
Renee Cattell (2019)
Identification de programmes d'activation macrophagique et microgliale dans les formes progressives de la sclérose en plaques
A. L’Huillier (2014)
10.1007/s00330-018-5539-3
A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules
Tingdan Hu (2018)
10.1016/j.jneumeth.2018.09.020
Correlating new directional measures of myelin and axonal integrity in T2-weighted MRI with quantitative histology in multiple sclerosis
Shrushrita Sharma (2019)
10.1371/journal.pone.0145497
Texture Analysis of T2-Weighted MR Images to Assess Acute Inflammation in Brain MS Lesions
N. Michoux (2015)
10.1186/s12885-015-1563-8
Texture analysis on MR images helps predicting non-response to NAC in breast cancer
N. Michoux (2015)
10.1016/j.mri.2013.10.006
Active inflammation increases the heterogeneity of MRI texture in mice with relapsing experimental allergic encephalomyelitis.
Y. Zhang (2014)
10.1111/jon.12262
MRI Texture Analysis Reveals Deep Gray Nuclei Damage in Amyotrophic Lateral Sclerosis
M. Albuquerque (2016)
10.1002/jmri.26904
Reliability of 3D texture analysis: A multicenter MRI study of the brain
Daniel Ta (2019)
10.21236/ada611615
In Vivo Imaging of Cortical Inflammation and Subpial Pathology in Multiple Sclerosis by Combined PET and MRI
C. Mainero (2015)
10.1017/cjn.2018.267
Texture Analysis to Detect Cerebral Degeneration in Amyotrophic Lateral Sclerosis
Abdullah Ishaque (2018)
10.5072/PRISM/25691
Development of tissue directionality-based measures of demyelination and remyelination for multiple sclerosis using structure tensor analysis
Mohammad hadi Kamalpour-Ansari (2014)
Grey matter pathology in multiple sclerosis : in vivo and post mortem magnetic resonance imaging studies
Shp van de Pavert (2017)
10.1007/s11547-020-01318-4
Radiomic analysis of the optic nerve at the first episode of acute optic neuritis: an indicator of optic nerve pathology and a predictor of visual recovery?
Michaela Cellina (2021)
10.1002/acn3.445
Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?
Francesco Grussu (2017)
10.1002/jmri.25460
Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer
Liheng Liu (2017)
10.3389/fneur.2021.626504
MRI Texture Analysis Reveals Brain Abnormalities in Medically Refractory Trigeminal Neuralgia
H. Danyluk (2021)
Predicting non-response to NAC in patients with breast cancer using 3D texture analysis
N. Michoux (2015)
10.1016/j.neurad.2014.05.006
Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome.
C. P. Loizou (2015)
10.1016/j.mri.2020.08.022
Normal appearing brain white matter changes in relapsing multiple sclerosis: Texture image and classification analysis in serial MRI scans.
C. P. Loizou (2020)
10.1002/jmri.26287
Quantitative radiomic biomarkers for discrimination between neuromyelitis optica spectrum disorder and multiple sclerosis
Xiaoxiao Ma (2019)
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