deepmriprep: voxel-based morphometry preprocessing via deep neural networks
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<sub>1</sub>-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.
Authors
- A. Kraus
- Benjamin Risse (ORCID: https://orcid.org/0000-0001-5691-4029)
- L. Fisch (ORCID: https://orcid.org/0000-0001-6424-7676)
- Andreas Jansen (ORCID: https://orcid.org/0000-0002-3140-8207)
- K. Berger
- Elisabeth Johanna Leehr (ORCID: https://orcid.org/0000-0002-9264-5003)
- Ramona Leenings
- L. Teutenberg
- Jan Ernsting (ORCID: https://orcid.org/0000-0003-4649-2111)
- Daniel Emden (ORCID: https://orcid.org/0000-0001-7459-6634)
- Susanne Meinert
- Maximilian Konowski (ORCID: https://orcid.org/0009-0009-2790-9330)
- Tiana Borgers
- C. Barkhau
- Nils Ralf Winter
- Tim Hahn
- Janik Goltermann
- Tilo Kircher
- Hamidreza Jamalabadi
- Kira Flinkenflügel
- Benjamin Straube
- Frederike Stein
- Paula Usemann
- Udo Dannlowski
- Dominik Grotegerd
- Igor Nenadić
- Florian Thomas-Odenthal
Institutions
- Philipps University of Marburg (DE)
- University of Münster (DE)
- German Centre for Cardiovascular Research (DE)
- Franklin University (US)
- FH Münster University of Applied Sciences (DE)
- Friedrich Schiller University Jena (DE)
- Bethel University (US)
Publication Details
- Journal
- Nature Computational Science
- Published
- 2026-01-30
- DOI
- https://doi.org/10.1038/s43588-026-00953-7
- Citations
- 1
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 36.80
Funders
- Deutsche Forschungsgemeinschaft
- Westfälische Wilhelms-Universität Münster