dentate nucleus t1 hyperintensity in multiple sclerosis

Gray matter (GM) damage, in terms of focal lesions,[1][1] "diffuse" tissue injury, and atrophy is a well-known feature of multiple sclerosis (MS). Recently, T1-hyperintensity on unenhanced T1-weighted sequences has been found in the dentate nuclei of patients with MS with severe disability an

spatial normalization and regional assessment of cord atrophy voxel based analysis of cervical cord 3d t1 weighted images

BACKGROUND AND PURPOSE: VBM is widely applied to characterize regional differences in brain volume among groups of subjects. The aim of this study was to develop and validate a method for voxelwise statistical analysis of cord volume and to test, with this method, the correlation between cord tissue loss and aging. MATERIALS AND METHODS: 3D T1-weighted scans of the spinal cord were acquired from 90 healthy subjects spanning several decades of life. Using an AS method, we outlined the cord surface and created output images reformatted with image planes perpendicular to the estimated cord centerline. Unfolded cervical cord images were coregistered into a common standard space, and smoothed cord binary masks, produced by using the cord outlines estimated by the AS approach, were used as input images for spatial statistics. RESULTS: High spatial correlation between normalized images was observed. Averaging of the normalized scans allowed the creation of a cervical cord template and of a standardized region-of-interest atlas. VBM analysis showed some significant associations between a decreased probability of cord tissue and aging. Results were robust across different smoothing levels, but the use of an anisotropic Gaussian kernel gave the optimal trade-off between spatial resolution and the requirements of the Gaussian random field theory. CONCLUSIONS: VBM analysis of the cervical cord was feasible and holds great promise for accurate localization of regional cord atrophy in several neurologic conditions

overcoming the clinical mr imaging paradox of multiple sclerosis mr imaging data assessed with a random forest approach

BACKGROUND AND PURPOSE: In MS, the relation between clinical and MR imaging measures is still suboptimal. We assessed the correlation of disability and specific impairment of the clinical functional system with overall and regional CNS damage in a large cohort of patients with MS with different clinical phenotypes by using a random forest approach. MATERIALS AND METHODS: Brain conventional MR imaging and DTI were performed in 172 patients with MS and 46 controls. Cervical cord MR imaging was performed in a subgroup of subjects. To evaluate whether MR imaging measures were able to correctly classify impairment in specific clinical domains, we performed a random forest analysis. RESULTS: Between-group differences were found for most of the MR imaging variables, which correlated significantly with clinical measures ( r ranging from −0.57 to 0.55). The random forest analysis showed a high performance in identifying impaired versus unimpaired patients, with a global error between 7% (pyramidal functional system) and 31% (Ambulation Index) in the different outcomes considered. When considering the performance in the unimpaired and impaired groups, the random forest analysis showed a high performance in identifying patients with impaired sensory, cerebellar, and brain stem functions (error below 10%), while it performed poorly in defining impairment of visual and mental systems (error of 91% and 70%, respectively). In analyses with a good level of classification, for most functional systems, damage of the WM fiber bundles subserving their function, measured by using DTI tractography, had the highest classification power. CONCLUSIONS: Random forest analysis, especially if applied to DTI tractography data, is a valuable approach, which might contribute to overcoming the MS clinical−MR imaging paradox

RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis.

In multiple sclerosis (MS), the presence of a paramagnetic rim at the edge of non-gadolinium-enhancing lesions indicates perilesional chronic inflammation. Patients featuring a higher paramagnetic rim lesion burden tend to have more aggressive disease. The objective of this study was to develop and evaluate a convolutional neural network (CNN) architecture (RimNet) for automated detection of paramagnetic rim lesions in MS employing multiple magnetic resonance (MR) imaging contrasts. Imaging data were acquired at 3 Tesla on three different scanners from two different centers, totaling 124 MS patients, and studied retrospectively. Paramagnetic rim lesion detection was independently assessed by two expert raters on T2*-phase images, yielding 462 rim-positive (rim+) and 4857 rim-negative (rim-) lesions. RimNet was designed using 3D patches centered on candidate lesions in 3D-EPI phase and 3D FLAIR as input to two network branches. The interconnection of branches at both the first network blocks and the last fully connected layers favors the extraction of low and high-level multimodal features, respectively. RimNet's performance was quantitatively evaluated against experts' evaluation from both lesion-wise and patient-wise perspectives. For the latter, patients were categorized based on a clinically relevant threshold of 4 rim+ lesions per patient. The individual prediction capabilities of the images were also explored and compared (DeLong test) by testing a CNN trained with one image as input (unimodal). The unimodal exploration showed the superior performance of 3D-EPI phase and 3D-EPI magnitude images in the rim+/- classification task (AUC = 0.913 and 0.901), compared to the 3D FLAIR (AUC = 0.855, Ps < 0.0001). The proposed multimodal RimNet prototype clearly outperformed the best unimodal approach (AUC = 0.943, P < 0.0001). The sensitivity and specificity achieved by RimNet (70.6% and 94.9%, respectively) are comparable to those of experts at the lesion level. In the patient-wise analysis, RimNet performed with an accuracy of 89.5% and a Dice coefficient (or F1 score) of 83.5%. The proposed prototype showed promising performance, supporting the usage of RimNet for speeding up and standardizing the paramagnetic rim lesions analysis in MS

Central vein sign differentiates Multiple Sclerosis from central nervous system inflammatory vasculopathies.

In multiple sclerosis (MS), magnetic resonance imaging (MRI) is a sensitive tool for detecting white matter lesions, but its diagnostic specificity is still suboptimal; ambiguous cases are frequent in clinical practice. Detection of perivenular lesions in the brain (the "central vein sign") improves the pathological specificity of MS diagnosis, but comprehensive evaluation of this MRI biomarker in MS-mimicking inflammatory and/or autoimmune diseases, such as central nervous system (CNS) inflammatory vasculopathies, is lacking. In a multicenter study, we assessed the frequency of perivenular lesions in MS versus systemic autoimmune diseases with CNS involvement and primary angiitis of the CNS (PACNS). In 31 patients with inflammatory CNS vasculopathies and 52 with relapsing-remitting MS, 3-dimensional T2*-weighted and T2-fluid-attenuated inversion recovery images were obtained during a single MRI acquisition after gadolinium injection. For each lesion, the central vein sign was evaluated according to consensus guidelines. For each patient, lesion count, volume, and brain location, as well as fulfillment of dissemination in space MRI criteria, were assessed. MS showed higher frequency of perivenular lesions (median = 88%) than did inflammatory CNS vasculopathies (14%), without overlap between groups or differences between 3T and 1.5T MRI. Among inflammatory vasculopathies, Behçet disease showed the highest median frequency of perivenular lesions (34%), followed by PACNS (14%), antiphospholipid syndromes (12%), Sjögren syndrome (11%), and systemic lupus erythematosus (0%). When a threshold of 50% perivenular lesions was applied, central vein sign discriminated MS from inflammatory vasculopathies with a diagnostic accuracy of 100%. The central vein sign differentiates inflammatory CNS vasculopathies from MS at standard clinical magnetic field strengths. Ann Neurol 2018;83:283-294

Pediatric multiple sclerosis: update on diagnostic criteria, imaging, histopathology and treatment choices

Pediatric multiple sclerosis (MS) represents less than 5% of the MS population, but patients with pediatric-onset disease reach permanent disability at a younger age than adult onset patients. Accurate diagnosis at presentation and optimal long-term treatment is vital to mitigate ongoing neuroinflammation and irreversible neurodegeneration. However, it may be difficult to early differentiate pediatric MS from acute disseminated encephalomyelitis (ADEM) and neuromyelitis optica spectrum disorders (NMOSD) as they often have atypical presentation that differs from that of adult-onset MS. The purpose of this review is to summarize the updated views on diagnostic criteria, imaging, histopathology and treatment choices