The incidence of neurodegenerative diseases has shown an increasing trend. These conditions typically cause progressive functional disability. Identification of robust biomarkers of neurodegenerative diseases is a key imperative to facilitate early identification of the pathological features and to foster a better understanding of the pathogenetic mechanisms of individual diseases. Diffusion tensor imaging (DTI) is the most widely used diffusion MRI technique for assessment of neurodegenerative diseases. The DTI parameters are promising biomarkers for evaluation of microstructural changes; however, some limitations of DTI restrict its wider clinical use. New diffusion MRI techniques, such as diffusion kurtosis imaging (DKI), bi‐tensor DTI, and neurite orientation density and dispersion imaging (NODDI) have been demonstrated to provide value addition to DTI for evaluation of neurodegenerative diseases. In this review article, we summarize the key technical aspects and provide an overview of the current state of knowledge regarding the role of DKI, bi‐tensor DTI, and NODDI as biomarkers of microstructural changes in representative neurodegenerative diseases including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease.Level of Evidence: 5Technical Efficacy Stage: 2 J. MAGN. RESON. IMAGING 2020;52:1620–1636.

BACKGROUND AND PURPOSE: Synthetic MR imaging enables the creation of various contrast-weighted images including double inversion recovery and phase-sensitive inversion recovery from a single MR imaging quantification scan. Here, we assessed whether synthetic MR imaging is suitable for detecting MS plaques. MATERIALS AND METHODS: Quantitative and conventional MR imaging data on 12 patients with MS were retrospectively analyzed. Synthetic T2-weighted, FLAIR, double inversion recovery, and phase-sensitive inversion recovery images were produced after quantification of T1 and T2 values and proton density. Double inversion recovery images were optimized for each patient by adjusting the TI. The number of visible plaques was determined by a radiologist for a set of these 4 types of synthetic MR images and a set of conventional T1-weighted inversion recovery, T2-weighted, and FLAIR images. Conventional 3D double inversion recovery and other available images were used as the criterion standard. The total acquisition time of synthetic MR imaging was 7 minutes 12 seconds and that of conventional MR imaging was 6 minutes 29 seconds The lesion-to-WM contrast and lesion-to-WM contrast-to-noise ratio were calculated and compared between synthetic and conventional double inversion recovery images. RESULTS: The total plaques detected by synthetic and conventional MR images were 157 and 139, respectively (P = .014). The lesion-to-WM contrast and contrast-to-noise ratio on synthetic double inversion recovery images were superior to those on conventional double inversion recovery images (P = .001 and < 0.001, respectively). CONCLUSIONS: Synthetic MR imaging enabled detection of more MS plaques than conventional MR imaging in a comparable acquisition time. The contrast for MS plaques on synthetic double inversion recovery images was better than on conventional double inversion recovery images.

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.

The glymphatic system is a glial-based perivascular network that promotes brain metabolic waste clearance. Reduced glymphatic flow has been observed in rat models of type 2 diabetes and hypertension, indicating the role of vascular risk factors in the glymphatic system. However, little is known about how vascular risk factors affect the human glymphatic system. The present study aims to assess the relationships between metabolic syndrome (MetS), a cluster of vascular risk factors, and the glymphatic system function using diffusion magnetic resonance imaging (MRI)-based measures of water diffusivity in the glymphatic compartments, including the brain interstitial space and perivascular spaces around the deep medullary vein. We hypothesized that vascular risk factors are associated with glymphatic dysfunction, leading to cognitive impairment in older adults. This cross-sectional study assessed 61 older adults (age range, 65-82 years) who had participated in the Bunkyo Health Study, including 15 healthy controls (mean age, 70.87 ± 4.90 years) and 46 individuals with MetS (mean age, 71.76 ± 4.61 years). Fractional volume of extracellular-free water (FW) and an index of diffusion tensor imaging along the perivascular space (DTI-ALPS) were used as indirect indicators of water diffusivity in the interstitial extracellular and perivenous spaces of white matter, respectively. After adjusting for age, sex, years of education, total Fazekas scale, Pittsburgh sleep quality index (PSQI) score, and intracranial volume (ICV), a significantly (P = 0.030; Cohen's d = 1.01) higher FW was observed in individuals with MetS than in the healthy controls. Furthermore, individuals with MetS had a significantly (P = 0.031; Cohen's d = 0.86) lower ALPS index than the healthy controls, with age, sex, years of education, total Fazekas scale, PSQI score, ICV, fractional anisotropy, and mean diffusivity included as confounding factors. Higher FW was significantly associated with lower ALPS index (r = -0.37; P = 0.004). Multiple linear regression (MLR) with backward elimination analyses showed that higher diastolic blood pressure (BP; standardized β = 0.33, P = 0.005) was independently associated with higher FW, whereas higher fasting plasma glucose levels (standardized β = -0.63, P = 0.002) or higher Brinkman index of cigarette consumption cumulative amount (standardized β = -0.27, P = 0.022) were associated with lower ALPS index. The lower ALPS index (standardized β, 0.28; P = 0.040) was associated with poorer global cognitive performance, which was determined using the Japanese version of the Montreal Cognitive Assessment (MOCA-J) scores. Finally, partial correlation analyses showed a significant correlation between higher FW and lower MOCA-J scores (r = -0.35; P = 0.025) and between higher FW and higher diastolic BP (r = 0.32, P = 0.044). The present study shows the changes in diffusion MRI-based measures reflected by the higher FW and lower ALPS index in older adults with MetS, possibly due to the adverse effect of vascular risk factors on the glymphatic system. Our findings also indicate the associations between the diffusion MRI-based measures and elevated diastolic BP, hyperglycemia, smoking habit, and poorer cognitive performance. However, owing to the limitations of this study, the results should be cautiously interpreted.

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 < .001). The Dice index of lesion maps and visual lesion conspicuity were comparable between generated and synthetic FLAIR images (P = 1 and .59, respectively). Generated FLAIR images showed fewer granular artifacts (P = .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.

The comprehension of the glymphatic system, a postulated mechanism responsible for the removal of interstitial solutes within the central nervous system (CNS), has witnessed substantial progress recently. While direct measurement techniques involving fluorescence and contrast agent tracers have demonstrated success in animal studies, their application in humans is invasive and presents challenges. Hence, exploring alternative noninvasive approaches that enable glymphatic research in humans is imperative. This review primarily focuses on several noninvasive magnetic resonance imaging (MRI) techniques, encompassing perivascular space (PVS) imaging, diffusion tensor image analysis along the PVS, arterial spin labeling, chemical exchange saturation transfer, and intravoxel incoherent motion. These methodologies provide valuable insights into the dynamics of interstitial fluid, water permeability across the blood–brain barrier, and cerebrospinal fluid flow within the cerebral parenchyma. Furthermore, the review elucidates the underlying concept and clinical applications of these noninvasive MRI techniques, highlighting their strengths and limitations. It addresses concerns about the relationship between glymphatic system activity and pathological alterations, emphasizing the necessity for further studies to establish correlations between noninvasive MRI measurements and pathological findings. Additionally, the challenges associated with conducting multisite studies, such as variability in MRI systems and acquisition parameters, are addressed, with a suggestion for the use of harmonization methods, such as the combined association test (COMBAT), to enhance standardization and statistical power. Current research gaps and future directions in noninvasive MRI techniques for assessing the glymphatic system are discussed, emphasizing the need for larger sample sizes, harmonization studies, and combined approaches. In conclusion, this review provides invaluable insights into the application of noninvasive MRI methods for monitoring glymphatic system activity in the CNS. It highlights their potential in advancing our understanding of the glymphatic system, facilitating clinical applications, and paving the way for future research endeavors in this field.Evidence Level: 3Technical Efficacy: Stage 5