| 1. |
- Herranz, E, et al.
(författare)
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Profiles of cortical inflammation in multiple sclerosis by 11C-PBR28 MR-PET and 7 Tesla imaging
- 2020
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Ingår i: Multiple sclerosis (Houndmills, Basingstoke, England). - : SAGE Publications. - 1477-0970 .- 1352-4585. ; 26:12, s. 1497-1509
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Tidskriftsartikel (refereegranskat)abstract
- Neuroinflammation with microglia activation is thought to be closely related to cortical multiple sclerosis (MS) lesion pathogenesis. Objective: Using 11C-PBR28 and 7 Tesla (7T) imaging, we assessed in 9 relapsing–remitting multiple sclerosis (RRMS) and 10 secondary progressive multiple sclerosis (SPMS) patients the following: (1) microglia activation in lesioned and normal-appearing cortex, (2) cortical lesion inflammatory profiles, and (3) the relationship between neuroinflammation and cortical integrity. Methods: Mean 11C-PBR28 uptake was measured in focal cortical lesions, cortical areas with 7T quantitative T2* (q-T2*) abnormalities, and normal-appearing cortex. The relative difference in cortical 11C-PBR28 uptake between patients and 14 controls was used to classify cortical lesions as either active or inactive. Disease burden was investigated according to cortical lesion inflammatory profiles. The relation between q-T2* and 11C-PBR28 uptake along the cortex was assessed. Results: 11C-PBR28 uptake was abnormally high in cortical lesions in RRMS and SPMS; in SPMS, tracer uptake was significantly increased also in normal-appearing cortex. 11C-PBR28 uptake and q-T2* correlated positively in many cortical areas, negatively in some regions. Patients with high cortical lesion inflammation had worse clinical outcome and higher intracortical lesion burden than patients with low inflammation. Conclusion: 11C-PBR28 and 7T imaging reveal distinct profiles of cortical inflammation in MS, which are related to disease burden.
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| 2. |
- Mangeat, G., et al.
(författare)
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Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis
- 2020
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Ingår i: Journal of Neuroimaging. - : Blackwell Publishing Inc.. - 1051-2284 .- 1552-6569. ; 30:5, s. 674-682
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND AND PURPOSE: Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and multiple sclerosis (MS) are demyelinating and neurodegenerative disorders that can be hard to distinguish clinically and radiologically. HDLS is a rare disorder compared to MS, which has led to occurrent misdiagnosis of HDLS as MS. That is problematic since their prognosis and treatment differ. Both disorders are investigated by MRI, which could help to identify patients with high probability of having HDLS, which could guide targeted genetic testing to confirm the HDLS diagnosis. METHODS: Here, we present a machine learning method based on quantitative MRI that can achieve a robust classification of HDLS versus MS. Four HDLS and 14 age-matched MS patients underwent a quantitative brain MRI protocol (synthetic MRI) at 3 Tesla (T) (scan time '7 minutes). We also performed a repeatability analysis of the predicting features to assess their generalizability by scanning a healthy control with five scan-rescans at 3T and 1.5T. RESULTS: Our predicting features were measured with an average confidence interval of 1.7% (P =.01), at 3T and 2.3% (P =.01) at 1.5T. The model gave a 100% correct classification of the cross-validation data when using 5-11 predicting features. When the maximum measurement noise was inserted in the model, the true positive rate of HDLS was 97.2%, while the true positive rate of MS was 99.6%. CONCLUSIONS: This study suggests that computer-assistance in combination with quantitative MRI may be helpful in aiding the challenging differential diagnosis of HDLS versus MS.
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| 3. |
- Ouellette, R., et al.
(författare)
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Validation of Rapid Magnetic Resonance Myelin Imaging in Multiple Sclerosis
- 2020
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Ingår i: Annals of Neurology. - : Wiley. - 0364-5134 .- 1531-8249. ; 87:5, s. 710-724
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Magnetic resonance imaging (MRI) is essential for multiple sclerosis diagnostics but is conventionally not specific to demyelination. Myelin imaging is often hampered by long scanning times, complex postprocessing, or lack of clinical approval. This study aimed to assess the specificity, robustness, and clinical value of Rapid Estimation of Myelin for Diagnostic Imaging, a new myelin imaging technique based on time-efficient simultaneous T1/T2 relaxometry and proton density mapping in multiple sclerosis. Methods: Rapid myelin imaging was applied using 3T MRI ex vivo in 3 multiple sclerosis brain samples and in vivo in a prospective cohort of 71 multiple sclerosis patients and 21 age/sex-matched healthy controls, with scan–rescan repeatability in a subcohort. Disability in patients was assessed by the Expanded Disability Status Scale and the Symbol Digit Modalities Test at baseline and 2-year follow-up. Results: Rapid myelin imaging correlated with myelin-related stains (proteolipid protein immunostaining and Luxol fast blue) and demonstrated good precision. Multiple sclerosis patients had, relative to controls, lower normalized whole-brain and normal-appearing white matter myelin fractions, which correlated with baseline cognitive and physical disability. Longitudinally, these myelin fractions correlated with follow-up physical disability, even with correction for baseline disability. Interpretation: Rapid Estimation of Myelin for Diagnostic Imaging provides robust myelin quantification that detects diffuse demyelination in normal-appearing tissue in multiple sclerosis, which is associated with both cognitive and clinical disability. Because the technique is fast, with automatic postprocessing and US Food and Drug Administration/CE clinical approval, it can be a clinically feasible biomarker that may be suitable to monitor myelin dynamics and evaluate treatments aiming at remyelination.
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