Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

deepmriprep: voxel-based morphometry preprocessing via deep neural networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月30日

查看全文
  • Lukas Fisch,
  • Nils R. Winter,
  • Janik Goltermann,
  • Carlotta Barkhau,
  • Daniel Emden,
  • Jan Ernsting,
  • Maximilian Konowski,
  • Ramona Leenings,
  • Tiana Borgers,
  • Kira Flinkenflügel,
  • Dominik Grotegerd,
  • Anna Kraus,
  • Elisabeth J. Leehr,
  • Susanne Meinert,
  • Frederike Stein,
  • Lea Teutenberg,
  • Florian Thomas-Odenthal,
  • Paula Usemann,
  • Marco Hermesdorf,
  • Hamidreza Jamalabadi,
  • Andreas Jansen,
  • Igor Nenadić,
  • Benjamin Straube,
  • Tilo Kircher,
  • Klaus Berger,
  • Benjamin Risse,
  • Udo Dannlowski,
  • Tim Hahn

摘要

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.