The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework — robust to variability in both image parameters and clinical condition — for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1,042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n=30). Data spanned three contrasts (T1-, T2-, and T2*-weighted) for a total of 1,943 volumes and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p≤0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of −15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.
Background and Purpose To investigate the potential of ultra-high field MR imaging to distinguish multiple sclerosis (MS) from neuromyelitis optica (NMO) and to characterize tissue injury associated with iron pathology within lesions. Methods Twenty-one MS and 21 NMO patients underwent 7T high-resolution 2D-gradient-echo (GRE-T2*) and 3D-susceptibility weighted imaging (SWI). An in-house developed algorithm was used to reconstruct quantitative susceptibility mapping (QSM) from SWI. Lesions were classified as ‘iron laden’ if they demonstrated hypointensity on GRE-T2*- weighted images and/or SWI, and hyperintensity on QSM. Lesions were considered ‘non-iron laden’ if they were hyperintense on GRE-T2* and isointense or hyperintense on QSM. Results Of 21 MS patients, 19 (90.5%) demonstrated atleast one QSM-hyperintense lesion and 11/21 (52.4%) patients harbored iron-laden lesions. No QSM-hyperintense or iron-laden lesions were observed in any of the NMO patients. Iron-laden and non iron-laden lesions could each be further characterized into two distinct patterns based on lesion signal and morphology on GRE-T2*/SWI and QSM. In MS, the majority of lesions (n=262, 75.9% of all lesions) were hyperintense on GRE-T2* and isointense on QSM (Pattern A), while a small minority (n=26, 7.5% of all lesions) were hyperintense on both GRE-T2* and QSM (Pattern B). Iron laden lesions (n=57, 16.5% of all lesions) were further classified as ‘nodular’ (n=22, 6.4%, Pattern C) or ‘ring-like’ (n=35, 10.1%, Pattern D). Conclusions Ultra-high field MRI may be useful in distinguishing MS from NMO. Different patterns related to iron and non-iron pathology may provide in vivo insights into pathophysiology of lesions in MS.
Background Neurological manifestations are common in patients with COVID-19, but little is known about pathophysiological mechanisms. In this single-center study, we describe neurological manifestations of 58 patients, regarding cerebrospinal fluid (CSF) analysis and neuroimaging findings. Methods 58 COVID-19 patients with neurologic manifestations and SARS-CoV-2 RT-PCR screening on CSF analysis were included. Clinical, laboratory, and brain MRI data were retrospectively collected and analyzed. Results Patients were mostly men (66%) with a median age of 62 years. Encephalopathy was frequent (81%), followed by a pyramidal dysfunction (16%), seizures (10%), and headaches (5%). Protein and albumin levels in CSF were increased in 38% and 23%, respectively. A total of 40% of patients displayed an elevated albumin quotient suggesting impaired blood-brain barrier integrity. CSF-specific IgG oligoclonal band was found in five (11%) cases, suggesting an intrathecal synthesis of IgG, and 26 (55%) patients presented identical oligoclonal bands in serum and CSF. Four (7%) patients harbored a positive SARS-CoV-2 RT-PCR in CSF. Regarding brain MRI, 20 (38%) patients presented leptomeningeal enhancement. Conclusions Brain MRI abnormalities, especially leptomeningeal enhancement, and increased inflammatory markers in CSF are frequent in patients with neurological manifestations related to COVID-19, whereas SARS-CoV 2 detection in CSF remained scanty.
Background and Purpose-MR signal changes after intravenous ultrasmall superparamagnetic iron oxide (USPIO) injection are related to inflammatory cells at the subacute stages after focal cerebral injury. However, at the early stages, the interpretation of USPIO-related MR signal alterations remains controversial. Here, we compared MR signal changes after intravenous USPIO injection with the histological iron and macrophage distribution during the first 24 hours in a rodent model of acute stroke. Methods-Multiparametric MRI at 7T and histological USPIO distribution were confronted from 6 to 24 hours after permanent middle cerebral artery occlusion in mice. Blood-brain barrier disruption was assessed using gadolinium MRI and immunoglobulin staining. Prussian blue staining was performed to depict the USPIO brain distribution. USPIO uptake by phagocytes was assessed by immunochemistry on brain tissue, peripheral blood cells, and monocyte cells derived from bone marrow culture. Results-After USPIO injection, 4 areas of early signal change were observed on every MRI. In all these areas, iron particles were mostly free whether detected in the vascular and cerebrospinal fluid compartments or in the interstitium. Within the first 24 hours, USPIO-loaded cells were not detected in the blood of injured mice or in cultured monocytic cells incubated with USPIO at plasmatic concentration. Conclusions-These
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