ArticleHuman brain mapping2023
Reliable brain morphometry from contrast-enhanced T1w-MRI in patients with multiple sclerosis.
Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Fast surface reconstruction of human brain MRI: benchmarking deep-learning based morphometry tools.Scientific reports · 2026Article
- Mitigating the Impact of MR Sequence Parameters: Increasing the Robustness of DL-Based Cortical Thickness Estimates.Human brain mapping · 2026Article
- Resources are associated with functional outcome and brain morphometry in childhood cancer survivors.Frontiers in oncology · 2026Article
- Machine learning-based reconstruction of 2D MRI for quantitative morphometry in epilepsy.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Diffeomorphic transformer-based abdomen MRI-CT deformable image registration.Medical physics · 2024Article
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- Three-dimensional infrared scanning: an enhanced approach for spatial registration of probes for neuroimaging.Neurophotonics · 2024Article
- Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods.NeuroImage. Clinical · 2024Article
- Clinical Evaluation of a Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis.Clinical neuroradiology · 2023Article
- Automatic brain structure segmentation forQuantitative imaging in medicine and surgery · 2023Article
- Reliable brain morphometry from contrast-enhanced T1w-MRI in patients with multiple sclerosis.Human brain mapping · 2023Article
- Multiple sclerosis as a model to investigate SARS-CoV-2 effect on brain atrophy.CNS neuroscience & therapeutics · 2023Article
- Distinct and shared patterns of brain plasticity during electroconvulsive therapy and treatment as usual in depression: an observational multimodal MRI-study.Translational psychiatry · 2023Observational
- Assessment of the Impact of Turbo Factor on Image Quality and Tissue Volumetrics in Brain Magnetic Resonance Imaging Using the Three-Dimensional T1-Weighted (3D T1W) Sequence.International journal of biomedical imaging · 2023Article
- Case report: Psychosis and catatonia in an adolescent patient with adipsic hypernatremia and autoantibodies against the subfornical organ.Frontiers in psychiatry · 2023Article
- Large-scale transient peri-ictal perfusion magnetic resonance imaging abnormalities detected by quantitative image analysis.Brain communications · 2023Article
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12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Brain morphometry is usually based on non-enhanced (pre-contrast) T1-weighted MRI. However, such dedicated protocols are sometimes missing in clinical examinations. Instead, an image with a contrast agent is often available. Existing tools such as FreeSurfer yield unreliable results when applied to contrast-enhanced (CE) images. Consequently, these acquisitions are excluded from retrospective morphometry studies, which reduces the sample size. We hypothesize that deep learning (DL)-based morphometry methods can extract morphometric measures also from contrast-enhanced MRI. We have extended DL+DiReCT to cope with contrast-enhanced MRI. Training data for our DL-based model were enriched with non-enhanced and CE image pairs from the same session. The segmentations were derived with FreeSurfer from the non-enhanced image and used as ground truth for the coregistered CE image. A longitudinal dataset of patients with multiple sclerosis (MS), comprising relapsing remitting (RRMS) and primary progressive (PPMS) subgroups, was used for the evaluation. Global and regional cortical thickness derived from non-enhanced and CE images were contrasted to results from FreeSurfer. Correlation coefficients of global mean cortical thickness between non-enhanced and CE images were significantly larger with DL+DiReCT (r = 0.92) than with FreeSurfer (r = 0.75). When comparing the longitudinal atrophy rates between the two MS subgroups, the effect sizes between PPMS and RRMS were higher with DL+DiReCT both for non-enhanced (d = -0.304) and CE images (d = -0.169) than for FreeSurfer (non-enhanced d = -0.111, CE d = 0.085). In conclusion, brain morphometry can be derived reliably from contrast-enhanced MRI using DL-based morphometry tools, making additional cases available for analysis and potential future diagnostic morphometry tools.
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