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Objective:To assess the prevalence and the specificity of leptomeningeal enhancement (LME) on postcontrast T2–fluid-attenuated inversion recovery (FLAIR) MRI in multiple sclerosis (MS) compared to a variety of inflammatory and noninflammatory neurologic conditions assessed in 2 academic research hospitals.Methods:On 3T postcontrast T2-FLAIR images, the presence of focal gadolinium enhancement was evaluated in the leptomeningeal compartment in 254 people with non-MS neurologic conditions or neurotropic viral infections. Based on their clinical diagnosis, patients were grouped as follows: (1) other-than-MS inflammatory neurologic diseases; (2) noninflammatory neurologic diseases; (3) human T-lymphotropic virus (HTLV)–infected; (4) HIV-infected; (5) healthy volunteers.Results:LME was detected in 56/254 non-MS cases (22%) vs 74/299 (25%) of MS cases. LME was nearly 4-fold more frequent in non-MS inflammatory neurologic conditions (18/51 cases, 35%) than in noninflammatory neurologic conditions (3/38, 8%) and healthy volunteers (5/66, 8%). The highest prevalence of LME was detected in HTLV infection (17/38 cases, 45%), particularly in the setting of HTLV-associated myelopathy (14/25 cases, 56%). LME also frequently occurred in HIV infection (13/61 cases, 21%). Unlike in MS, LME is not associated with lower brain and cortical volumes in non-MS inflammatory neurologic conditions, including HTLV and HIV infection.Conclusions:Despite its relevance to MS pathogenesis and cortical pathology, LME is not specific to MS, occurring frequently in non-MS inflammatory neurologic conditions and especially in those patients with HTLV-associated myelopathy. Overall, this strengthens the notion that LME localizes inflammation-related focal disruption of the blood–meninges barrier and associated scarring.

Background Brain- and lesion-volumes derived from magnetic resonance images (MRI) serve as important imaging markers of disease progression in neurodegenerative diseases and aging. While manual segmentation of these volumes is both tedious and impractical in large cohorts of subjects, automated segmentation methods often fail in accurate segmentation of brains with severe atrophy or high lesion loads. The purpose of this study was to develop an atlas-free brain Classification using DErivative-based Features (C-DEF), which utilizes all scans that may be acquired during the course of a routine MRI study at any center. Methods Proton-density, T2-weighted, T1-weighted, brain-free water, 3D FLAIR, 3D T2-weighted, and 3D T2*-weighted images, collected routinely on patients with neuroinflammatory diseases at the NIH, were used to optimize the C-DEF algorithm on healthy volunteers and HIV + subjects (cohort 1). First, manually marked lesions and eroded FreeSurfer brain segmentation masks (compiled into gray and white matter, globus pallidus, CSF labels) were used in training. Next, the optimized C-DEF was applied on a separate cohort of HIV + subjects (cohort two), and the results were compared with that of FreeSurfer and Lesion-TOADS. Finally, C-DEF segmentation was evaluated on subjects clinically diagnosed with various other neurological diseases (cohort three). Results C-DEF algorithm was optimized using leave-one-out cross validation on five healthy subjects (age 36 ± 11 years), and five subjects infected with HIV (age 57 ± 2.6 years) in cohort one. The optimized C-DEF algorithm outperformed FreeSurfer and Lesion-TOADS segmentation in 49 other subjects infected with HIV (cohort two, age 54 ± 6 years) in qualitative and quantitative comparisons. Although trained only on HIV brains, sensitivity to detect lesions using C-DEF increased by 45% in HTLV-I-associated myelopathy/tropical spastic paraparesis (n = 5; age 58 ± 7 years), 33% in multiple sclerosis (n = 5; 42 ± 9 years old), and 4% in subjects with polymorphism of the cytotoxic T-lymphocyte-associated protein 4 gene (n = 5; age 24 ± 12 years) compared to Lesion-TOADS. Conclusion C-DEF outperformed other segmentation algorithms in the various neurological diseases explored herein, especially in lesion segmentation. While the results reported are from routine images acquired at the NIH, the algorithm can be easily trained and optimized for any set of contrasts and protocols for wider application. We are currently exploring various technical aspects of optimal implementation of CDEF in a clinical setting and evaluating a larger cohort of patients with other neurological diseases. Improving the accuracy of brain segmentation methodology will help better understand the relationship of imaging abnormalities to clinical and neuropsychological markers in disease.