Comparison of myelin water fraction values in periventricular white matter lesions between multiple sclerosis and neuromyelitis optica spectrum disorder

Jeong, Ilgyu; Choi, Joon Yul; Kim, Suhyun; Hyun, Jae‐Won; Joung, AeRan; Lee, Jong-Ho; Kim, Ho Jin

doi:10.1177/1352458516636247

Cited by 23 publications

(22 citation statements)

References 19 publications

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“…Much of the pioneering in vivo work in myelin water imaging has been studies of MS. MS plaques, or lesions, show variably decreased MWF (Figure 7) (38, 58,64,85,93,97,122,166,171,180), averaging approximately half that of Figure 2). normal-appearing white matter (NAWM) (85).…”

Section: Multiple Sclerosis Plaquesmentioning

confidence: 99%

Myelin water imaging to detect demyelination and remyelination and its validation in pathology

Laule

Moore

2018

Brain Pathology

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Damage to myelin is a key feature of multiple sclerosis (MS) pathology. Magnetic resonance imaging (MRI) has revolutionized our ability to detect and monitor MS pathology in vivo. Proton density, T1 and T2 can provide qualitative contrast weightings that yield superb in vivo visualization of central nervous system tissue and have proved invaluable as diagnostic and patient management tools in MS. However, standard clinical MR methods are not specific to the types of tissue damage they visualize, and they cannot detect subtle abnormalities in tissue that appears otherwise normal on conventional MRIs. Myelin water imaging is an MR method that provides in vivo measurement of myelin. Histological validation work in both human brain and spinal cord tissue demonstrates a strong correlation between myelin water and staining for myelin, validating myelin water as a marker for myelin. Myelin water varies throughout the brain and spinal cord in healthy controls, and shows good intra‐ and inter‐site reproducibility. MS plaques show variably decreased myelin water fraction, with older lesions demonstrating the greatest myelin loss. Longitudinal study of myelin water can provide insights into the dynamics of demyelination and remyelination in plaques. Normal appearing brain and spinal cord tissues show reduced myelin water, an abnormality which becomes progressively more evident over a timescale of years. Diffusely abnormal white matter, which is evident in 20%–25% of MS patients, also shows reduced myelin water both in vivo and postmortem, and appears to originate from a primary lipid abnormality with relative preservation of myelin proteins. Active research is ongoing in the quest to refine our ability to image myelin and its perturbations in MS and other disorders of the myelin sheath.

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Section: Multiple Sclerosis Plaquesmentioning

confidence: 99%

Myelin water imaging to detect demyelination and remyelination and its validation in pathology

Laule

Moore

2018

Brain Pathology

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“…Then voxel-wise multiecho data were processed to generate a T2 distribution by fitting stimulated-echo corrected multi-exponential functions 7 . The following parameters, which were common for clinical studies [18][19][20][21] , were used for the fitting: number of exponential functions = 120; T2 range = 15 to 2000 ms, logarithmically spaced; and chisquare regularization with the constraint of 1.020 2 ≤ 2 ≤ 1.025 2 . From the T2 distribution, MWF was calculated by dividing the sum of the signals from 15 to 40 ms by the sum of the entire T2 distribution.…”

Section: Conventional Mwimentioning

confidence: 99%

Artificial neural network for myelin water imaging

Lee

Choi

et al. 2019

Magnetic Resonance in Med

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Purpose:To demonstrate the application of artificial-neural-network (ANN) for real-time processing of myelin water imaging (MWI).Methods: Three neural networks, ANN-IMWF, ANN-IGMT2, and ANN-II, were developed to generate MWI. ANN-IMWF and ANN-IGMT2 were designed to output myelin water fraction (MWF) and geometric mean T2 (GMT2,IEW), respectively whereas ANN-II generates a T2 distribution.For the networks, gradient and spin echo data from 18 healthy controls (HC) and 26 multiple sclerosis patients (MS) were utilized. Among them, 10 HC and 12 MS had the same scan parameters and were used for training (6 HC and 6 MS), validation (1 HC and 1 MS), and test sets (3 HC and 5 MS). The remaining data had different scan parameters and were applied to exam the effects of the scan parameters. The network results were compared with those of conventional MWI in the white matter mask and regions of interest (ROI). Results:The networks produced highly accurate results, showing averaged normalized root-mean-squared error under 3% for MWF and 0.4% for GMT2,IEW in the white matter mask of the test set. In the ROI analysis, the differences between ANNs and conventional MWI were less than 0.1% in MWF and 0.1 ms in GMT2,IEW (no statistical difference and R 2 > 0.97). Datasets with different scan parameters showed increased errors. The average processing time was 0.68 sec in ANNs, gaining 11,702 times acceleration in the computational speed (conventional MWI: 7,958 sec). Conclusion:The proposed neural networks demonstrate the feasibility of real-time processing for MWI with high accuracy. K E Y W O R D SMyelin water imaging, artificial neural network, T2 distribution, multiecho spin echo, multiple sclerosis, deep learning NRF-2017M3C7A1047864).The Institute of Engineering Research at Seoul National University provided research facilities for this work.This work is an extension of a previously published ISMRM abstract of the same authors 44 .

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“…Myelin water fraction (MWF), defined as the fraction of the total signal which has T 2 < 40 milliseconds at 3 Tesla, has been histologically validated as a marker of myelin density in human tissue, 30–32 and has good scan‐rescan reproducibility (coefficient of variation 4% in cerebral white matter) 33 . MWI has been used to investigate myelin in a wide range of diseases including multiple sclerosis, neuromyelitis optica spectrum disorder, schizophrenia, Niemann‐Pick disease, prenatal alcohol exposure, amyotrophic and primary lateral sclerosis, Krabbe disease, autism, stroke, and traumatic brain injury 34–47 . Rapid myelination has been demonstrated in early childhood and adolescence using multicomponent driven equilibrium single pulse observation of T 1 and T 2 (mcDESPOT), an alternate approach to MWI using a steady‐state acquisition 48–53 .…”

Section: Introductionmentioning

confidence: 99%

Brain Myelin Water Fraction and Diffusion Tensor Imaging Atlases for 9‐10 Year‐Old Children

Morris

Holmes

Dvorak

et al. 2020

Journal of Neuroimaging

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BACKGROUND AND PURPOSE Myelin water imaging (MWI) and diffusion tensor imaging (DTI) provide information about myelin and axon‐related brain microstructure, which can be useful for investigating normal brain development and many childhood brain disorders. While pediatric DTI atlases exist, there are no pediatric MWI atlases available for the 9‐10 years old age group. As myelination and structural development occurs throughout childhood and adolescence, studies of pediatric brain pathologies must use age‐specific MWI and DTI healthy control data. We created atlases of myelin water fraction (MWF) and DTI metrics for healthy children aged 9‐10 years for use as normative data in pediatric neuroimaging studies. METHODS 3D‐T1, DTI, and MWI scans were acquired from 20 healthy children (mean age: 9.6 years, range: 9.2‐10.3 years, 4 females). ANTs and FSL registration were used to create quantitative MWF and DTI atlases. Region of interest (ROI) analysis in nine white matter regions was used to compare pediatric MWF with adult MWF values from a recent study and to investigate the correlation between pediatric MWF and DTI metrics. RESULTS Adults had significantly higher MWF than the pediatric cohort in seven of the nine white matter ROIs, but not in the genu of the corpus callosum or the cingulum. In the pediatric data, MWF correlated significantly with mean diffusivity, but not with axial diffusivity, radial diffusivity, or fractional anisotropy. CONCLUSIONS Normative MWF and DTI metrics from a group of 9‐10 year old healthy children provide a resource for comparison to pathologies. The age‐specific atlases are ready for use in pediatric neuroimaging research and can be accessed: https://sourceforge.net/projects/pediatric-mri-myelin-diffusion/.

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Comparison of myelin water fraction values in periventricular white matter lesions between multiple sclerosis and neuromyelitis optica spectrum disorder

Cited by 23 publications

References 19 publications

Myelin water imaging to detect demyelination and remyelination and its validation in pathology

Myelin water imaging to detect demyelination and remyelination and its validation in pathology

Artificial neural network for myelin water imaging

Brain Myelin Water Fraction and Diffusion Tensor Imaging Atlases for 9‐10 Year‐Old Children

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