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.

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.

Cognitive impairment could affect quality of life for patients with multiple sclerosis (MS), and cognitive function may be correlated with several factors such as depression and fatigue. This study aimed to evaluate cognitive function in Japanese patients with MS and the association between cognitive function and apathy, fatigue, and depression. The Brief Repeatable Battery of Neuropsychological tests (BRB-N) was performed in 184 Japanese patients with MS and 163 healthy controls matched for age, gender, and education. The Apathy Scale (AS), Fatigue Questionnaire (FQ), and Beck Depression Inventory Second Edition (BDI-II) were used to evaluate apathy, fatigue, and depression, respectively. Student’s t-test was used to compare MS patients and healthy controls. Correlations between two factors were assessed using the Pearson correlation test, and multiple regression analysis was used to evaluate how much each factor affected the BRB-N score. In all BRB-N tests, patients with MS scored significantly lower than controls, and the effect size of symbol digit modalities test was the highest among the 9 tests of the BRB-N. Patients with MS had higher AS (p < 0.001), FQ (p < 0.0001), and BDI-II (p < 0.0001) scores than controls. In patients with MS, scores on most of the BRB-N tests correlated with scores on the AS and BDI-II; however, there was little correlation between scores on the BRB-N tests and those on the FQ. Cognitive function was impaired, particularly information-processing speed, and decreased cognitive function was correlated with apathy and depression in Japanese patients with MS. Despite the association between cognitive variables and depression/apathy, cognitive function was impaired beyond the effect of depression and apathy. However, subjective fatigue is not related with cognitive impairment. Taken together, this suggests that different therapeutic approaches are needed to improve subjective fatigue and cognition, and thereby quality of life, in patients with MS.

BACKGROUND AND PURPOSE: T1 and T2 values and proton density can now be quantified on the basis of a single MR acquisition. The myelin and edema in a voxel can also be estimated from these values. The purpose of this study was to evaluate a multiparametric quantitative MR imaging model that assesses myelin and edema for characterizing plaques, periplaque white matter, and normal-appearing white matter in patients with MS. MATERIALS AND METHODS: We examined 3T quantitative MR imaging data from 21 patients with MS. The myelin partial volume, excess parenchymal water partial volume, the inverse of T1 and transverse T2 relaxation times (R1, R2), and proton density were compared among plaques, periplaque white matter, and normal-appearing white matter. RESULTS: All metrics differed significantly across the 3 groups (P < .001). Those in plaques differed most from those in normal-appearing white matter. The percentage changes of the metrics in plaques and periplaque white matter relative to normal-appearing white matter were significantly more different from zero for myelin partial volume (mean, −61.59 ± 20.28% [plaque relative to normal-appearing white matter], and mean, −10.51 ± 11.41% [periplaque white matter relative to normal-appearing white matter]), and excess parenchymal water partial volume (13.82 × 10³ ± 49.47 × 10³% and 51.33 × 10² ± 155.31 × 10²%) than for R1 (−35.23 ± 13.93% and −6.08 ± 8.66%), R2 (−21.06 ± 11.39% and −4.79 ± 6.79%), and proton density (23.37 ± 10.30% and 3.37 ± 4.24%). CONCLUSIONS: Multiparametric quantitative MR imaging captures white matter damage in MS. Myelin partial volume and excess parenchymal water partial volume are more sensitive to the MS disease process than R1, R2, and proton density.

BACKGROUND AND PURPOSE: A number of MR-derived quantitative metrics have been suggested to assess the pathophysiology of MS, but the reports about combined analyses of these metrics are scarce. Our aim was to assess the spatial distribution of parameters for white matter myelin and axon integrity in patients with relapsing-remitting MS by multiparametric MR imaging. MATERIALS AND METHODS: Twenty-four patients with relapsing-remitting MS and 24 age- and sex-matched controls were prospectively scanned by quantitative synthetic and 2-shell diffusion MR imaging. Synthetic MR imaging data were used to retrieve relaxometry parameters (R1 and R2 relaxation rates and proton density) and myelin volume fraction. Diffusion tensor metrics (fractional anisotropy and mean, axial, and radial diffusivity) and neurite orientation and dispersion index metrics (intracellular volume fraction, isotropic volume fraction, and orientation dispersion index) were retrieved from diffusion MR imaging data. These data were analyzed using Tract-Based Spatial Statistics. RESULTS: Patients with MS showed significantly lower fractional anisotropy and myelin volume fraction and higher isotropic volume fraction in widespread white matter areas. Areas with different isotropic volume fractions were included within areas with lower fractional anisotropy. Myelin volume fraction showed no significant difference in some areas with significantly decreased fractional anisotropy in MS, including in the genu of the corpus callosum and bilateral anterior corona radiata, whereas myelin volume fraction was significantly decreased in some areas where fractional anisotropy showed no significant difference, including the bilateral posterior limb of the internal capsule, external capsule, sagittal striatum, fornix, and uncinate fasciculus. CONCLUSIONS: We found differences in spatial distribution of abnormality in fractional anisotropy, isotropic volume fraction, and myelin volume fraction distribution in MS, which might be useful for characterizing white matter in patients with MS.

BACKGROUND AND PURPOSE: Myelin and axon volume fractions can now be estimated via MR imaging in vivo, as can the g-ratio, which equals the ratio of the inner to the outer diameter of a nerve fiber. The purpose of this study was to evaluate WM damage in patients with MS via this novel MR imaging technique. MATERIALS AND METHODS: Twenty patients with relapsing-remitting MS with a combined total of 149 chronic plaques were analyzed. Myelin volume fraction was calculated based on simultaneous tissue relaxometry. Intracellular and CSF compartment volume fractions were quantified via neurite orientation dispersion and density imaging. Axon volume fraction and g-ratio were calculated by combining these measurements. Myelin and axon volume fractions and g-ratio were measured in plaques, periplaque WM, and normal-appearing WM. RESULTS: All metrics differed significantly across the 3 groups (P < .001, except P = .027 for g-ratio between periplaque WM and normal-appearing WM). Those in plaques differed most from those in normal-appearing WM. The percentage changes in plaque and periplaque WM metrics relative to normal-appearing WM were significantly larger in absolute value for myelin volume fraction than for axon volume fraction and g-ratio (P < .001, except P = .033 in periplaque WM relative to normal-appearing WM for comparison between myelin and axon volume fraction). CONCLUSIONS: In this in vivo MR imaging study, the myelin of WM was more damaged than axons in plaques and periplaque WM of patients with MS. Myelin and axon volume fractions and g-ratio may potentially be useful for evaluating WM damage in patients with MS.