56 research outputs found

    Inter-Vendor Reproducibility of Myelin Water Imaging Using a 3D Gradient and Spin Echo Sequence

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    Myelin water imaging can be achieved using multicomponent T2 relaxation analysis to quantify in vivo measurement of myelin content, termed the myelin water fraction (MWF). Therefore, myelin water imaging can be a valuable tool to better understand the underlying white matter pathology in demyelinating diseases, such as multiple sclerosis. To apply myelin water imaging in multisite studies and clinical applications, it must be acquired in a clinically feasible scan time (less than 15 min) and be reproducible across sites and scanner vendors. Here, we assessed the reproducibility of MWF measurements in regional and global white matter in 10 healthy human brains across two sites with two different 3 T magnetic resonance imaging scanner vendors (Philips and Siemens), using a 32-echo gradient and spin echo (GRASE) sequence. A strong correlation was found between the MWF measurements in the global white matter (Pearsonโ€™s r = 0.91; p < 0.001) for all participants across the two sites. The mean intersite MWF coefficient of variation across participants was 2.77% in the global white matter and ranged from 4.47% (splenium of the corpus callosum) to 17.89% (genu of the corpus callosum) in white matter regions of interest. Bland-Altman analysis showed a good agreement in MWF measurements between the two sites with small bias of 0.002. Overall, MWF estimates were in good agreement across the two sites and scanner vendors. Our findings support the use of quantitative multi-echo T2 relaxation metrics, such as the MWF, in multicenter studies and clinical trials to gain deeper understanding about the pathological processes resulting from the underlying disease progression in neurodegenerative diseases

    Motor Skill Acquisition Promotes Human Brain Myelin Plasticity

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    Quantitative MRI in leukodystrophies

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    Leukodystrophies constitute a large and heterogeneous group of genetic diseases primarily affecting the white matter of the central nervous system. Different disorders target different white matter structural components. Leukodystrophies are most often progressive and fatal. In recent years, novel therapies are emerging and for an increasing number of leukodystrophies trials are being developed. Objective and quantitative metrics are needed to serve as outcome measures in trials. Quantitative MRI yields information on microstructural properties, such as myelin or axonal content and condition, and on the chemical composition of white matter, in a noninvasive fashion. By providing information on white matter microstructural involvement, quantitative MRI may contribute to the evaluation and monitoring of leukodystrophies. Many distinct MR techniques are available at different stages of development. While some are already clinically applicable, others are less far developed and have only or mainly been applied in healthy subjects. In this review, we explore the background, current status, potential and challenges of available quantitative MR techniques in the context of leukodystrophies

    ์ž„์ƒ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์‹ ๊ฒฝ์ˆ˜์ดˆ๋ฌผ์˜์ƒ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2019. 2. ์ด์ข…ํ˜ธ.์‹ ๊ฒฝ์ˆ˜์ดˆ๋Š” ๋ชธ ์•ˆ์˜ ์ „๊ธฐ์  ์‹ ํ˜ธ๋ฅผ ์ „๋‹ฌํ•˜๋Š”๋ฐ ์žˆ์–ด ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ์‹ ๊ฒฝํ‡ดํ–‰์„ฑ์งˆํ™˜์€ ์‹ ๊ฒฝ์ˆ˜์ดˆ ์†์ƒ๊ณผ ์—ฐ๊ด€์„ฑ์ด ์žˆ์œผ๋ฉฐ ์ด๋Š” ์ „๊ธฐ์  ์‹ ํ˜ธ ์ „๋‹ฌ์˜ ์†์‹ค์„ ์œ ๋ฐœํ•œ๋‹ค. ๋ณ‘์›์—์„œ ์‚ฌ์šฉํ•˜๋Š” ์ž๊ธฐ ๊ณต๋ช… ์˜์ƒ๋ฒ•์ธ T1, T2 ๊ฐ•์กฐ์˜์ƒ๋“ค์€ ์‹ ๊ฒฝ์ˆ˜์ดˆ์˜ ์–‘์„ ์ •๋Ÿ‰ํ™” ํ•  ์ˆ˜ ์—†๊ณ  ์‹ ๊ฒฝํ‡ดํ–‰์„ฑ์งˆํ™˜ ํ™˜์ž์˜ ์‹ ๊ฒฝ์ˆ˜์ดˆ์˜ ์†์ƒ๋œ ์ •๋„๋ฅผ ํ™•์ธ ํ•  ์ˆ˜ ์—†๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‹ ๊ฒฝ์ˆ˜์ดˆ์˜ ์†์ƒ๋œ ์ •๋„๋ฅผ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•ด ์ƒˆ๋กญ๊ฒŒ ๊ฐœ๋ฐœ ๋œ ์‹ ๊ฒฝ์ˆ˜์ดˆ๋ฌผ์˜์ƒ์„ ์‹ ๊ฒฝํ‡ดํ–‰์„ฑ์งˆํ™˜์— ์ ์šฉํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์‹ ๊ฒฝ๋‹ค๋ฐœ์˜ ๋ฌผ๊ตํ™˜ ๋ฐ ๋จธ๋ฆฌ๋กœ ์œ ์ž…๋˜๋Š” ํ˜ˆ๋ฅ˜๋กœ ์ธํ•œ ์‹ ๊ฒฝ์ˆ˜์ดˆ๋ฌผ์˜์ƒ๋ฒ•์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•˜์—ฌ ํƒ๊ตฌํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์‹ ๊ฒฝ์ˆ˜์ดˆ๋ฌผ์˜์ƒ๋ฒ•์„ ์ด์šฉํ•œ ์ž„์ƒ์  ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•˜์—ฌ ๋ถ„์„ ํŒŒ์ดํ”„๋ผ์ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ฒซ์งธ๋กœ ์‹ ๊ฒฝ๋‹ค๋ฐœ์˜ ์ƒ๋ฌผ, ๋ฌผ๋ฆฌ์ ํ•™์  ํŠน์„ฑ์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ™”ํ•œ Monte-Carlo ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์—์„œ ๊ณ„์‚ฐ๋œ ์‹ ๊ฒฝ์ˆ˜์ดˆ๋ฌผ์˜ ๊ฑฐ์ฃผ ์‹œ๊ฐ„์„ ์ด์šฉํ•˜์—ฌ ์‹ ๊ฒฝ์ˆ˜์ดˆ๋ฌผ์˜์ƒ๋ฒ•์ด ์‹ ๊ฒฝ์ˆ˜์ดˆ๋ฌผ์„ ์ธก์ •ํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋‘˜์งธ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋จธ๋ฆฌ๋กœ ์œ ์ž…๋˜๋Š” ํ˜ˆ๋ฅ˜๋กœ ์ธํ•œ artifact์„ ์ œ๊ฑฐํ•˜๊ธฐ ์œ„ํ•ด ํ˜ˆ๋ฅ˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ํ˜ˆ๋ฅ˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ชจ๋ธ์„ ํ†ตํ•˜์—ฌ ์œ ์ž…๋˜๋Š” ํ˜ˆ๋ฅ˜๋กœ ์ธํ•œ artifact์„ ์ตœ์†Œํ™” ํ•˜๋Š” ํ˜ˆ๋ฅ˜ํฌํ™”ํŽ„์Šค์˜ ์ตœ์  ์‹œ๊ฐ„์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ตœ์ข…์ ์œผ๋กœ, ์‹ ๊ฒฝ์ˆ˜์ดˆ๋ฌผ ์˜์ƒ์˜ ์ž„์ƒ์—ฐ๊ตฌ ์ ์šฉ์„ ์œ„ํ•˜์—ฌ ๋ถ„์„ ํŒŒ์ดํ”„ ๋ผ์ธ์„ ๊ฐœ๋ฐœ ๋ฐ ์š”์•ฝํ•˜์˜€๋‹ค. ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ์‹ ๊ฒฝํ‡ดํ–‰์„ฑ์งˆํ™˜์ธ ๋‹ค๋ฐœ์„ฑ๊ฒฝํ™”์ฆ, ์‹œ์‹ ๊ฒฝ์ฒ™์ˆ˜์—ผ, ์™ธ์ƒ์„ฑ ๋‡Œ์†์ƒ ํ™˜์ž์˜ ์ •์ƒ์œผ๋กœ ๋ณด์ด๋Š” ์˜์—ญ์—์„œ ์‹ ๊ฒฝ์ˆ˜์ดˆ๋ฌผ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ์ถ”ํ›„ ์ˆ˜์ดˆ๊ด€๋ จ ๋‡Œ ์งˆํ™˜์˜ ์ง„๋‹จ, ์น˜๋ฃŒ์˜ ํšจ์šฉ์„ฑ ๋ฐ ์˜ˆํ›„ ํ‰๊ฐ€๋ฟ ์•„๋‹ˆ๋ผ ํ•™์Šต์— ์˜ํ•œ ๋‡Œ ๊ฐ€์†Œ์„ฑ ์—ฐ๊ตฌ ๋ฐ ์žฌํ™œ ์น˜๋ฃŒ ํšจ๊ณผ ํ‰๊ฐ€์— ์ด์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ผ ์‚ฌ๋ฃŒ๋œ๋‹ค.Myelin plays an important role in transmitting electrical signals in the body. Neurodegenerative diseases are associated with myelin damage and induce a loss of the electrical signals. The conventional T1 and T2 weighted imaging, used in clinics, cannot quantify the amount of myelin and confirm the degree of myelin damage in patients with neurodegenerative diseases. This thesis applied newly developed myelin water imaging, named ViSTa, to the neurodegenerative diseases to estimate changes in myelin. To utilize ViSTa myelin water imaging in clinical studies, I explored the effects of water exchange and inflow in ViSTa myelin water imaging. Then, I developed new data analysis pipelines to apply ViSTa myelin water imaging for the clinical studies. First, the Monte-Carlo simulation model that has the biological and physical properties of white matter fiber was developed for myelin water residence time. The simulation model validated the origin of ViSTa as myelin water. Second, the thesis developed a flow simulation model to compensate artifacts from inflow blood in ViSTa myelin water imaging. The flow simulation model suggested the optimal timing of flow saturation pulse(s) to suppress the inflow of blood. Finally, I summarized new data analysis pipelines for clinical applications. Using the analysis pipelines, ViSTa myelin water imaging revealed reduced apparent myelin water fraction in normal-appearing white matter for three prominent brain diseases and injury (neurodegenerative diseases): multiple sclerosis, neuromyelitis optica spectrum disorders, and traumatic brain injury. The developments in this thesis can be utilized not only in the diagnosis, treatment, and prognosis of various diseases but also in neuroplasticity and rehabilitation studies to explore the answer for the questions related to myelin issues.Chapter 1. Introduction 1 1.1 Myelin 1 1.2 Myelin Water 1 1.3 ViSTa Myelin Water Imaging 4 1.4 Purpose of Study 7 Chapter 2. Water Exchange Model 8 2.1 Introduction 8 2.2 Methods 8 2.3 Results 14 2.4 Discussion 16 Chapter 3. Blood Flow Simulation Model 17 3.1 Introduction 17 3.2 Methods 18 3.3 Results 25 3.4 Discussion 30 Chapter 4. Clinical Applications 32 4.1 Multiple Sclerosis 32 4.1.1 Introduction 32 4.1.2 Methods 33 4.1.3 Results 42 4.1.4 Discussion 52 4.2 Neuromyelitis Optica Spectrum Disorder 56 4.2.1 Introduction 56 4.2.2 Methods 57 4.2.3 Results 60 4.2.4 Discussion 65 4.3 Traumatic Brain Injury 68 4.3.1 Introduction 68 4.3.2 Methods 69 4.3.3 Results 75 4.3.4 Discussion 80 Chapter 5. Conclusion 84 Reference 85 Abstract 100Docto

    Characterizing the microstructural basis of โ€œunidentified bright objectsโ€ in neurofibromatosis type 1:A combined in vivo multicomponent T2 relaxation and multi-shell diffusion MRI analysis

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    AbstractIntroductionThe histopathological basis of โ€œunidentified bright objectsโ€ (UBOs) (hyperintense regions seen on T2-weighted magnetic resonance (MR) brain scans in neurofibromatosis-1 (NF1)) remains unclear. New in vivo MRI-based techniques (multi-exponential T2 relaxation (MET2) and diffusion MR imaging (dMRI)) provide measures relating to microstructural change. We combined these methods and present previously unreported data on in vivo UBO microstructure in NF1.Methods3-Tesla dMRI data were acquired on 17 NF1 patients, covering 30 white matter UBOs. Diffusion tensor, kurtosis and neurite orientation and dispersion density imaging parameters were calculated within UBO sites and in contralateral normal appearing white matter (cNAWM). Analysis of MET2 parameters was performed on 24 UBOโ€“cNAWM pairs.ResultsNo significant alterations in the myelin water fraction and intra- and extracellular (IE) water fraction were found. Mean T2 time of IE water was significantly higher in UBOs. UBOs furthermore showed increased axial, radial and mean diffusivity, and decreased fractional anisotropy, mean kurtosis and neurite density index compared to cNAWM. Neurite orientation dispersion and isotropic fluid fraction were unaltered.ConclusionOur results suggest that demyelination and axonal degeneration are unlikely to be present in UBOs, which appear to be mainly caused by a shift towards a higher T2-value of the intra- and extracellular water pool. This may arise from altered microstructural compartmentalization, and an increase in โ€˜extracellular-likeโ€™, intracellular water, possibly due to intramyelinic edema. These findings confirm the added value of combining dMRI and MET2 to characterize the microstructural basis of T2 hyperintensities in vivo

    Myelin water imaging from multi-echo T-2 MR relaxometry data using a joint sparsity constraint

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    Demyelination is the key pathological process in multiple sclerosis (MS). The extent of demyelination can be quantified with magnetic resonance imaging by assessing the myelin water fraction (MWF). However, long computation times and high noise sensitivity hinder the translation of MWF imaging to clinical practice. In this work, we introduce a more efficient and noise robust method to determine the MWF using a joint sparsity constraint and a pre-computed B-1(+)-T-2 dictionary.A single component analysis with this dictionary is used in an initial step to obtain a B-1(+) map. The T-2 distribution is then determined from a reduced dictionary corresponding to the estimated B-1(+) map using a combination of a non-negativity and a joint sparsity constraint.The non-negativity constraint ensures that a feasible solution with non-negative contribution of each T-2 component is obtained. The joint sparsity constraint restricts the T-2 distribution to a small set of T-2 relaxation times shared between all voxels and reduces the noise sensitivity.The applied Sparsity Promoting Iterative Joint NNLS (SPIJN) algorithm can be implemented efficiently, reducing the computation time by a factor of 50 compared to the commonly used regularized non-negative least squares algorithm. The proposed method was validated in simulations and in 8 healthy subjects with a 3D multiecho gradient- and spin echo scan at 3 T. In simulations, the absolute error in the MWF decreased from 0.031 to 0.013 compared to the regularized NNLS algorithm for SNR = 250. The in vivo results were consistent with values reported in literature and improved MWF-quantification was obtained especially in the frontal white matter. The maximum standard deviation in mean MWF in different regions of interest between subjects was smaller for the proposed method (0.0193) compared to the regularized NNLS algorithm (0.0266). In conclusion, the proposed method for MWF estimation is less computationally expensive and less susceptible to noise compared to state of the art methods. These improvements might be an important step towards clinical translation of MWF measurements.Neuro Imaging Researc

    Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI

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    Age-related microstructural differences have been detected using diffusion tensor imaging (DTI). Although DTI is sensitive to the effects of aging, it is not specific to any underlying biological mechanism, including demyelination. Combining multiexponential T2 relaxation (MET2) and multishell diffusion MRI (dMRI) techniques may elucidate such processes. Multishell dMRI and MET2 data were acquired from 59 healthy participants aged 17-70 years. Whole-brain and regional age-associated correlations of measures related to multiple dMRI models (DTI, diffusion kurtosis imaging [DKI], neurite orientation dispersion and density imaging [NODDI]) and myelin-sensitive MET2 metrics were assessed. DTI and NODDI revealed widespread increases in isotropic diffusivity with increasing age. In frontal white matter, fractional anisotropy linearly decreased with age, paralleled by increased "neurite" dispersion and no difference in myelin water fraction. DKI measures and neurite density correlated well with myelin water fraction and intracellular and extracellular water fraction. DTI estimates remain among the most sensitive markers for age-related alterations in white matter. NODDI, DKI, and MET2 indicate that the initial decrease in frontal fractional anisotropy may be due to increased axonal dispersion rather than demyelination

    Model-Informed Machine Learning for Multi-component T2 Relaxometry

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    Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to combine machine learning and aspects of parametric (fitting from the MRI signal using biophysical models) and non-parametric (model-free fitting of the T2 distribution from the signal) approaches to T2 relaxometry in brain tissue by using a multi-layer perceptron (MLP) for the distribution reconstruction. For training our network, we construct an extensive synthetic dataset derived from biophysical models in order to constrain the outputs with \textit{a priori} knowledge of \textit{in vivo} distributions. The proposed approach, called Model-Informed Machine Learning (MIML), takes as input the MR signal and directly outputs the associated T2 distribution. We evaluate MIML in comparison to non-parametric and parametric approaches on synthetic data, an ex vivo scan, and high-resolution scans of healthy subjects and a subject with Multiple Sclerosis. In synthetic data, MIML provides more accurate and noise-robust distributions. In real data, MWF maps derived from MIML exhibit the greatest conformity to anatomical scans, have the highest correlation to a histological map of myelin volume, and the best unambiguous lesion visualization and localization, with superior contrast between lesions and normal appearing tissue. In whole-brain analysis, MIML is 22 to 4980 times faster than non-parametric and parametric methods, respectively.Comment: Preprint submitted to Medical Image Analysis (July 14, 2020
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