ArticleGigaScience2025
Segmentation-based quality control of structural MRI using the CAT12 toolbox.
Article in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Comment on "AI-based CT quantification reveals small airway loss and vascular simplification in rheumatoid arthritis-associated lung disease".Clinical rheumatology · 2026Article
- Identification of MRI-Derived Structural Biomarkers in Female Alzheimer's Disease Subjects Using CAT12 and Mimics: Effects of Voxel Geometry on Biomarker Estimation.Life (Basel, Switzerland) · 2026Article
- Observational
- Quantification of amyotrophic lateral sclerosis (ALS) disease accumulation with T1-weighted high-resolution magnetic resonance imaging: validation in an independent cohort.Journal of neurology · 2026Article
- Factors Contributing to Short-Term Structural Variability in a Longitudinal MRI Dataset.Human brain mapping · 2026Article
- Alterations in cortical surface morphometry in fibromyalgia: a multi-parametric study.Frontiers in psychology · 2026Article
- Multimodal MRI links gray matter volume loss and temporal lobe susceptibility to CSF immunological profiles in Guillain-Barré syndrome.Frontiers in immunology · 2026Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundThe processing and analysis of magnetic resonance images is highly dependent on the quality of the input data, and systematic differences in quality can consequently lead to loss of sensitivity or biased results. However, varying image properties due to different scanners and acquisition protocols, as well as subject-specific image interferences, such as motion artifacts, can be incorporated in the analysis. A reliable assessment of image quality is therefore essential to identify critical outliers that may bias results.
findingsHere, we present a quality assessment for structural (T1-weighted) images using tissue classification in the SPM/CAT12 ecosystem. We introduce multiple useful image quality measures, standardize them into quality scales, and combine them into an integrated structural image quality rating to facilitate the interpretation and fast identification of outliers with (motion) artifacts. The reliability and robustness of the measures are evaluated using synthetic and real datasets. Our study results demonstrate that the proposed measures are robust to simulated segmentation problems and variables of interest, such as cortical atrophy, age, sex, brain size, and severe disease-related changes, and might facilitate the separation of motion artifacts based on within-protocol deviations.
conclusionThe quality control framework presents a simple but powerful tool for the use in research and clinical settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.