OmniPrep is under active development — early documentation and usage preview.
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Multi-modal · Scalable · Reproducible

Unified Pipeline for Multimodal MRI Processing Across the Lifespan

OmniPrep unifies sMRI, dMRI, and fMRI preprocessing and downstream analysis, from atlas-based ROI features to connectomics, accelerated by deep learning and parallel computing.

Get Started Read the Docs
bash — omniprep
$ docker run -it --rm --gpus all\
    -v /path/to/bids:/data -v /path/to/output:/out\
    -v /path/to/license.txt:/license.txt\
    uxhao/omniprep:latest /data /out participant\
    --fs_license_file /license.txt --tasks "anat bold dwi"
Why OmniPrep

OmniPrep in a nutshell

One command from raw BIDS data to analysis-ready derivatives, across every MRI modality.

Unified Multimodal

Structural (sMRI), functional (fMRI), and diffusion (dMRI) preprocessing in a single, coherent workflow — no stitching tools together.

Built on Nextflow

Reproducible, containerized, and resumable. Scale from a laptop to HPC clusters and cloud with the same command line.

Atlas & ROI Derivatives

Built-in cortical atlases, subcortical parcellation, morphometric features, ROI time series, functional and structural connectomes.

Quality Control Built-in

Automated QC reports, trace, and timeline generated with every run — motion scrubbing, FD-based frame detection included.

Age-Aware Templates

Adult, pediatric, and infant cohorts with automatic age detection and matching MNI templates and distortion-correction strategies.

GPU Accelerated

Automatically leverages available GPUs for eligible steps, with transparent CPU fallback and a supervised GPU daemon.

3
MRI modalities
6+
Built-in atlases
60+
Configuration options
2
Container runtimes
Three modalities, one pipeline

End-to-end multimodal workflows

Enable the tasks you need with the --tasks flag — dependencies between modalities are handled automatically.

anat

Structural MRI

T1w preprocessing & reconstruction

FreeSurfer-based cortical reconstruction and segmentation, producing surfaces, parcellations, and atlas-level morphometric features.

  • FreeSurfer reconstruction
  • ROI-level morphometry
  • Native & MNI-space surfaces
Learn more →
bold

Functional MRI

BOLD preprocessing & derivatives

Full BOLD denoising chain with motion scrubbing, nuisance regression, temporal filtering, and a rich set of voxel- and ROI-level metrics.

  • ReHo, ALFF/fALFF, INT
  • ROI time series & FC matrices
  • CIFTI & surface outputs
Learn more →
dwi

Diffusion MRI

DWI preprocessing, tractography & connectomics

Distortion correction, DTI and CSD metrics, whole-brain tractography (iFOD2, SD-Stream, tensor), and structural connectomes.

  • DTI & CSD metrics
  • Whole-brain tractography
  • Structural connectomes
Learn more →
Quick start

Run in three steps

From raw BIDS data to derivatives in minutes.

  1. Pull the container image

    Get the pre-built OmniPrep image from Docker Hub.

    bash
    docker pull uxhao/omniprep:latest

  2. Prepare your BIDS dataset & license

    Point OmniPrep at your BIDS directory and a FreeSurfer license file.

  3. Launch the pipeline

    Run the workflow with the modalities you need.

    bash
    docker run -it --rm --gpus all \
  -v /path/to/bids:/data \
  -v /path/to/output:/out \
  -v /path/to/license.txt:/license.txt \
  uxhao/omniprep:latest /data /out participant \
  --fs_license_file /license.txt \
  --tasks "anat bold dwi"

What you get

OmniPrep writes a BIDS-derivatives-compatible tree under output_dir/derivatives/omniprep, including:

Recon/ BOLD/ DWI/ QC/ report.html timeline.html
  • FreeSurfer reconstruction & morphometry
  • Denoised BOLD + voxel/ROI metrics
  • Tractography & connectomes
  • Automated QC reports
View full guide →

Ready to preprocess at scale?

Read the documentation to explore every option, or jump straight into installation.

Browse Documentation About the Project