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deepmriprep: voxel-based morphometry preprocessing via deep neural networks
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DOI：10.1038/s43588-026-00953-7.png)
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Abstract
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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 T1-weighted magnetic resonance imaging. Utilizing the graphics processing unit, deepmriprep is 37 times faster than CAT12, the leading VBM preprocessing toolbox. The proposed method matches CAT12 in accuracy for tissue segmentation and image registration across more than 100 datasets and shows strong correlations in the VBM results. Tissue segmentation maps from deepmriprep have more than 95% agreement with ground-truth maps, and its nonlinear registration predicts smooth deformation fields comparable to CAT12. The high computational speed of deepmriprep enables rapid preprocessing of large datasets and opens the door to real-time applications. deepmriprep leverages neural networks to enable voxel-based morphometry preprocessing of MRI data that is 37× faster than existing methods while achieving comparable accuracy in segmentation, registration and final statistical maps across large datasets.
Keywords:
Computational neuroscience
Machine learning
Computer Science
general
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