ArticleNeuroradiology2023
Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses.
Article in Neuroradiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 27 citations in OpenAlex.
- Volumetry of Selected Brain Regions-Can We Compare MRI Examinations of Different Manufacturers and Field Strengths?Clinical neuroradiology · 2025Article
- Repeatability and reproducibility of brain age estimates in multiple sclerosis for three publicly available models.Neuroimage. Reports · 2025Article
- Multiple Sclerosis-Specific Reference Curves for Brain Volumes to Explain Disease Severity.Neurology · 2025Observational
- The Effects of Disease-Modifying Therapies on Optic Nerve Degeneration in Multiple Sclerosis.European journal of neurology · 2025Review
- The role of AI for MRI-analysis in multiple sclerosis-A brief overview.Frontiers in artificial intelligence · 2025Review
- Predictive Value of Brain Volumetry in the Response to Subthalamotomy Using High-Intensity Focused Ultrasound Treatment in Patients With Parkinson's Disease.Parkinson's disease · 2025Article
- Multi-cohort, multi-sequence harmonisation for cerebrovascular brain age.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- FastSurfer parcellation accuracy after lesion filling in moderate-to-severe traumatic brain injury.Frontiers in neurology · 2025Article
- Artificial intelligence will make neuroradiology even more exciting.Neuroradiology · 2024Article
- Digital outcome measures are associated with brain atrophy in patients with multiple sclerosis.Journal of neurology · 2024Article
- Simulating rigid head motion artifacts on brain magnitude MRI data-Outcome on image quality and segmentation of the cerebral cortex.PloS one · 2024Article
- Feasibility of detecting atrophy relevant for disability and cognition in multiple sclerosis using 3D-FLAIR.Journal of neurology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeVolume measurement using MRI is important to assess brain atrophy in multiple sclerosis (MS). However, differences between scanners, acquisition protocols, and analysis software introduce unwanted variability of volumes. To quantify theses effects, we compared within-scanner repeatability and between-scanner reproducibility of three different MR scanners for six brain segmentation methods.
methodsTwenty-one people with MS underwent scanning and rescanning on three 3 T MR scanners (GE MR750, Philips Ingenuity, Toshiba Vantage Titan) to obtain 3D T1-weighted images. FreeSurfer, FSL, SAMSEG, FastSurfer, CAT-12, and SynthSeg were used to quantify brain, white matter and (deep) gray matter volumes both from lesion-filled and non-lesion-filled 3D T1-weighted images. We used intra-class correlation coefficient (ICC) to quantify agreement; repeated-measures ANOVA to analyze systematic differences; and variance component analysis to quantify the standard error of measurement (SEM) and smallest detectable change (SDC).
resultsFor all six software, both between-scanner agreement (ICCs ranging 0.4-1) and within-scanner agreement (ICC range: 0.6-1) were typically good, and good to excellent (ICC > 0.7) for large structures. No clear differences were found between filled and non-filled images. However, gray and white matter volumes did differ systematically between scanners for all software (p < 0.05). Variance component analysis yielded within-scanner SDC ranging from 1.02% (SAMSEG, whole-brain) to 14.55% (FreeSurfer, CSF); and between-scanner SDC ranging from 4.83% (SynthSeg, thalamus) to 29.25% (CAT12, thalamus).
conclusionVolume measurements of brain, GM and WM showed high repeatability, and high reproducibility despite substantial differences between scanners. Smallest detectable change was high, especially between different scanners, which hampers the clinical implementation of atrophy measurements.
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