ArticleNature computational science2026
deepmriprep: voxel-based morphometry preprocessing via deep neural networks.
Article in Nature computational science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Deep learning approach to super-resolution correction of brain MRI motion artifacts for accurate hippocampal volumetry.Scientific reports · 2026Article
- MICAFlow: Fast and robust MRI preprocessing bridging research neuroimaging and clinical practice.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Assessment of Small Vessel Density Changes in Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalophy (CADASIL) by High-Resolution Black-Blood MRI.Journal of magnetic resonance imaging : JMRI · 2026Article
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Authors and funding
28 authors.
Funding
Abstract
Voxel-based morphometry (VBM), a popular approach in neuroimaging research, uses magnetic resonance imaging data to assess variations in the local density of brain tissue and to examine its associations with biological and psychometric variables. Here we present deepmriprep, a preprocessing pipeline designed to leverage neural networks to perform all the necessary preprocessing steps for the VBM analysis of T
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Registered trials
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