ArticleInvestigative radiology2019
Automated Detection and Segmentation of Multiple Sclerosis Lesions Using Ultra-High-Field MP2RAGE.
Article in Investigative radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Imaging cortical multiple sclerosis lesions with ultra-high field MRI.NeuroImage. Clinical · 2021Pooled it
- Generalizable spinal cord multiple sclerosis lesion segmentation across MRI contrasts, protocols, and centers.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
- Advanced MRI Techniques: Diagnosis and Follow-Up of Multiple Sclerosis.Diagnostics (Basel, Switzerland) · 2024Review
- The use of 7T MRI in multiple sclerosis: review and consensus statement from the North American Imaging in Multiple Sclerosis Cooperative.Brain communications · 2024Review
- Present and future of the diagnostic work-up of multiple sclerosis: the imaging perspective.Journal of neurology · 2023Review
- Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI.NMR in biomedicine · 2022Article
- Predictive value of number and volume of demyelinating plaques in treatment response in patients with multiple sclerosis treated with INF-B.American journal of neurodegenerative disease · 2022Article
- Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects.NeuroImage. Clinical · 2022Article
- 7T MRI Differentiates Remyelinated from Demyelinated Multiple Sclerosis Lesions.Annals of neurology · 2021Article
- MRI with ultrahigh field strength and high-performance gradients: challenges and opportunities for clinical neuroimaging at 7 T and beyond.European radiology experimental · 2021Review
- An anomaly detection approach to identify chronic brain infarcts on MRI.Scientific reports · 2021Article
- Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks.Scientific reports · 2021Article
- 7T MPFLAIR versus MP2RAGE for Quantifying Lesion Volume in Multiple Sclerosis.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2020Article
- Improved Cervical Cord Lesion Detection with 3D-MP2RAGE Sequence in Patients with Multiple Sclerosis.AJNR. American journal of neuroradiology · 2020Article
- Gray matter network reorganization in multiple sclerosis from 7-Tesla and 3-Tesla MRI data.Annals of clinical and translational neurology · 2020Article
- Longitudinal analysis of white matter and cortical lesions in multiple sclerosis.NeuroImage. Clinical · 2019Article
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10 authors.
Funding
Abstract
objectivesThe aim of this study was to develop a new automated segmentation method of white matter (WM) and cortical multiple sclerosis (MS) lesions visible on magnetization-prepared 2 inversion-contrast rapid gradient echo (MP2RAGE) images acquired at 7 T MRI. MATERIALS AND
methodsThe proposed prototype (MSLAST [Multiple Sclerosis Lesion Analysis at Seven Tesla]) takes as input a single image contrast derived from the 7T MP2RAGE prototype sequence and is based on partial volume estimation and topological constraints. First, MSLAST performs a skull-strip of MP2RAGE images and computes tissue concentration maps for WM, gray matter (GM), and cerebrospinal fluid (CSF) using a partial volume model of tissues within each voxel. Second, MSLAST performs (1) connected-component analysis to GM and CSF concentration maps to classify small isolated components as MS lesions; (2) hole-filling in the WM concentration map to classify areas with low WM concentration surrounded by WM (ie, MS lesions); and (3) outlier rejection to the WM mask to improve the classification of small WM lesions. Third, MSLAST unifies the 3 maps obtained from 1, 2, and 3 processing steps to generate a global lesion mask.
resultsQuantitative and qualitative assessments were performed using MSLAST in 25 MS patients from 2 research centers. Overall, MSLAST detected a median of 71% of MS lesions, specifically 74% of WM and 58% of cortical lesions, when a minimum lesion size of 6 μL was considered. The median false-positive rate was 40%. When a 15 μL minimal lesions size was applied, which is the approximation of the minimal size recommended for 1.5/3 T images, the median detection rate was 80% for WM and 63% for cortical lesions, respectively, and the median false-positive rate was 33%. We observed high correlation between MSLAST and manual segmentations (Spearman rank correlation coefficient, ρ = 0.91), although MSLAST underestimated the total lesion volume (average difference of 1.1 mL), especially in patients with high lesion loads. MSLAST also showed good scan-rescan repeatability within the same session with an average absolute volume difference and F1 score of 0.38 ± 0.32 mL and 84%, respectively.
conclusionsWe propose a new methodology to facilitate the segmentation of WM and cortical MS lesions at 7 T MRI, our approach uses a single MP2RAGE scan and may be of special interest to clinicians and researchers.
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