Analysis of White Matter Damage in Patients with Multiple Sclerosis via a Novel In Vivo MR Method for Measuring Myelin, Axons, and G-Ratio

Hagiwara, Akifumi; Hori, Masaaki; Yokoyama, Kazumasa; Nakazawa, Masaharu; Ueda, Ryo; Horita, Moeko; Andica, Christina; Abe, Osamu; Aoki, Shigeki

doi:10.3174/ajnr.a5312

Cited by 46 publications

(50 citation statements)

References 42 publications

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“…NODDI metrics have been identified as useful diagnostic biomarkers for revealing microstructural changes in the brains of patients with Alzheimer's disease [10], Parkinson's disease [7,11,12], stroke [13], and multiple sclerosis [14,15]. Recently, NODDI has been used to differentiate brain tumors [16] and explore white matter (WM) microstructure in very preterm-born children [17].…”

Section: Introductionmentioning

confidence: 99%

Scan–rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics

et al. 2019

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Purpose The reproducibility of neurite orientation dispersion and density imaging (NODDI) metrics in the human brain has not been explored across different magnetic resonance (MR) scanners from different vendors. This study aimed to evaluate the scanrescan and inter-vendor reproducibility of NODDI metrics in white and gray matter of healthy subjects using two 3-T MR scanners from two vendors. Methods Ten healthy subjects (7 males; mean age 30 ± 7 years, range 23-37 years) were included in the study. Whole-brain diffusion-weighted imaging was performed with b-values of 1000 and 2000 s/mm 2 using two 3-T MR scanners from two different vendors. Automatic extraction of the region of interest was performed to obtain NODDI metrics for whole and localized areas of white and gray matter. The coefficient of variation (CoV) and intraclass correlation coefficient (ICC) were calculated to assess the scan-rescan and inter-vendor reproducibilities of NODDI metrics. Results The scan-rescan and inter-vendor reproducibility of NODDI metrics (intracellular volume fraction and orientation dispersion index) were comparable with those of diffusion tensor imaging (DTI) metrics. However, the inter-vendor reproducibilities of NODDI (CoV = 2.3-14%) were lower than the scan-rescan reproducibility (CoV: scanner A = 0.8-3.8%; scanner B = 0.8-2.6%). Compared with the finding of DTI metrics, the reproducibility of NODDI metrics was lower in white matter and higher in gray matter. Conclusion The lower inter-vendor reproducibility of NODDI in some brain regions indicates that data acquired from different MRI scanners should be carefully interpreted.

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Section: Introductionmentioning

confidence: 99%

Scan–rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics

et al. 2019

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“…The most important roles of magnetic resonance imaging (MRI) in demyelinating diseases include: (i) diagnosis; [1][2][3][4][5] (ii) imaging biomarkers; [5][6][7][8][9][10][11][12][13][14][15][16][17] and (iii) monitoring of sideeffects from disease-modifying drugs. The most important roles of magnetic resonance imaging (MRI) in demyelinating diseases include: (i) diagnosis; [1][2][3][4][5] (ii) imaging biomarkers; [5][6][7][8][9][10][11][12][13][14][15][16][17] and (iii) monitoring of sideeffects from disease-modifying drugs.…”

Section: Introductionmentioning

confidence: 99%

“…Multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), acute disseminated encephalomyelitis (ADEM) and myelin oligodendrocyte glycoprotein (MOG) encephalomyelitis are major inflammatory demyelinating diseases. The most important roles of magnetic resonance imaging (MRI) in demyelinating diseases include: (i) diagnosis; [1][2][3][4][5] (ii) imaging biomarkers; [5][6][7][8][9][10][11][12][13][14][15][16][17] and (iii) monitoring of sideeffects from disease-modifying drugs. [18][19][20][21] The present review will focus on the diagnostic role of MRI in these diseases.…”

Section: Introductionmentioning

confidence: 99%

Magnetic resonance imaging diagnosis of demyelinating diseases: An update

Miki

2019

Clinical & Exp Neuroim

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Keywords acute disseminating encephalomyelitis (ADEM); demyelinating diseases; multiple sclerosis (MS); myelin oligodendrocyte glycoprotein (MOG) encephalomyelitis; neuromyelitis optica spectrum disorder (NMOSD) Correspondence AbstractMajor demyelinating diseases include multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), acute disseminated encephalomyelitis (ADEM) and myelin oligodendrocyte glycoprotein (MOG) encephalomyelitis. As treatment strategies differ among these diseases, precise diagnosis of demyelinating diseases is crucial, and magnetic resonance imaging (MRI) plays a pivotal role in the diagnosis. In the present review, MRI appearances of characteristic lesions of these demyelinating diseases, and differentiation between MS and other demyelinating diseases based on MRI findings are discussed.

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“…1 Syn-thetic MR imaging has become clinically feasible due to the development of rapid and simultaneous relaxometric methods, 2 which have high repeatability and reproducibility across different vendors. 3 The synthetic MR imaging quality [4][5][6] and its clinical utility for evaluating brain diseases [7][8][9][10] have been widely investigated. Synthetic MR imaging has the potential to reduce scan times in clinical settings, where multiple contrast-weighted images are usually required.…”

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Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation

Hagiwara

Otsuka²,

Hori³

et al. 2019

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Synthetic FLAIR images are of lower quality than conventional FLAIR images. Here, we aimed to improve the synthetic FLAIR image quality using deep learning with pixel-by-pixel translation through conditional generative adversarial network training. MATERIALS AND METHODS:Forty patients with MS were prospectively included and scanned (3T) to acquire synthetic MR imaging and conventional FLAIR images. Synthetic FLAIR images were created with the SyMRI software. Acquired data were divided into 30 training and 10 test datasets. A conditional generative adversarial network was trained to generate improved FLAIR images from raw synthetic MR imaging data using conventional FLAIR images as targets. The peak signal-to-noise ratio, normalized root mean square error, and the Dice index of MS lesion maps were calculated for synthetic and deep learning FLAIR images against conventional FLAIR images, respectively. Lesion conspicuity and the existence of artifacts were visually assessed. RESULTS:The peak signal-to-noise ratio and normalized root mean square error were significantly higher and lower, respectively, in generated-versus-synthetic FLAIR images in aggregate intracranial tissues and all tissue segments (all P Ͻ .001). The Dice index of lesion maps and visual lesion conspicuity were comparable between generated and synthetic FLAIR images (P ϭ 1 and .59, respectively). Generated FLAIR images showed fewer granular artifacts (P ϭ .003) and swelling artifacts (in all cases) than synthetic FLAIR images. CONCLUSIONS:Using deep learning, we improved the synthetic FLAIR image quality by generating FLAIR images that have contrast closer to that of conventional FLAIR images and fewer granular and swelling artifacts, while preserving the lesion contrast.ABBREVIATIONS: cGAN ϭ conditional generative adversarial network; DL ϭ deep learning; GAN ϭ generative adversarial network; NRMSE ϭ normalized root mean square error; PSNR ϭ peak signal-to-noise ratio Indicates open access to non-subscribers at www.ajnr.org http://dx.

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Analysis of White Matter Damage in Patients with Multiple Sclerosis via a Novel In Vivo MR Method for Measuring Myelin, Axons, and G-Ratio

Cited by 46 publications

References 42 publications

Scan–rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics

Scan–rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics

Magnetic resonance imaging diagnosis of demyelinating diseases: An update

Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation

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