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DCEasy QUANTITATIVE DCE-MRI, END TO END

DCEasy is a set of open-source tools for quantitative dynamic contrast-enhanced MRI, maintained by the PET/MRI Lab at Loma Linda University. Together they cover the whole path from scanner output to parameter maps; each one is also usable on its own.

Where each tool fits

1 · Convert DICOM → BIDS dce2bids 2 · Preprocess motion · bias DCEPrep 3 · AIF input function AutoAIF / AIFArtist 4 · T1 map VFA / IR parametric_scripts 5 · PK fit Ktrans · ve · vp ROCKETSHIP + Gpufit 6 · Analyze compare · QC ROCKETSHIP

You can run the pipeline two ways.

End to end. dce2bids converts the study to BIDS, then DCEPrep takes it the rest of the way in one Dockerized run — preprocessing, T1 mapping, AIF detection, fitting and QC.

Step by step. Swap in your own tools at any stage, or use ours individually. The cards below say which tool covers each.

Tools

  • dce2bids Python · AI-assisted

    Converts raw DCE-MRI DICOMs straight from the scanner into a tidy BIDS dataset. An AI coding agent works out the right per-scanner settings once; after that each new participant converts with a single command, with guardrails and verification around it.

    Documentation Repository

  • DCEPrep Shell · Python · Docker

    The whole pipeline in one container: motion correction, bias field correction, z-axis normalization, VFA T1 mapping, AIF detection, Ktrans fitting ROCKETSHIP, and per-case and population QC reports.

    Documentation Repository

  • parametric_scripts MATLAB

    Generates T1 (inversion recovery and variable flip angle), T2, T2* and ADC maps, with a fitting GUI, batch processing and parallel fitting. Reads NIfTI and Analyze.

    Documentation Repository

  • AutoAIF Python · deep learning

    Finds the arterial input function automatically — no manual ROI drawing. Pretrained on multi-site brain DCE-MRI cohorts, outputs a vascular function curve and a 3D mask, and supports fine-tuning on new datasets.

    Documentation Repository Paper — MRM 2025

  • AIFArtist Python

    Manual AIF annotation when you want a human in the loop, or a multi-rater reference to check AutoAIF against. Live ROI curve preview, BIDS-style derivative outputs with the rater ID in the filename, and flag-and-skip for poor AIFs.

    Documentation Repository

  • ROCKETSHIP MATLAB

    A GUI-driven suite for full DCE-MRI analysis: pre-contrast T1 mapping, AIF selection and fitting, multi-model pharmacokinetic fitting (Tofts, Extended Tofts, Patlak, 2CXM, FXR, tissue uptake) and results visualization.

    Documentation Repository Paper — BMC Med Imaging 2015

  • Gpufit CUDA · C++

    Levenberg–Marquardt curve fitting on the GPU, with the DCE models built in — Patlak, Tofts, Extended Tofts, tissue uptake, 2CXM, T1 FA exponential. Full CPU parity, Python and MATLAB wrappers, prebuilt binaries. Useful for accelerating any DCE pipeline, not only ours.

    Repository

Which tool do I need?

I want to… Use
Convert DCE DICOMs to BIDS dce2bids
Run the complete pipeline on BIDS data, automatically DCEPrep
Run preprocessing only — motion, alignment, artifact correction DCEPrep
Generate T1, T2 or ADC maps parametric_scripts
Find the AIF automatically, with deep learning AutoAIF
Draw and save AIF ROIs by hand, across multiple raters AIFArtist
Fit pharmacokinetic models through a GUI ROCKETSHIP
Accelerate model fitting inside another pipeline Gpufit
Compare fit quality and parametric maps ROCKETSHIP — Module E

Quick start

docker pull lsaca05/dce:R2023a-main

docker run --rm \
  -v /path/to/rawdata:/data/rawdata \
  -v /path/to/matlab.lic:/licenses/matlab.lic \
  lsaca05/dce:R2023a-main \
  ./preprocess_all.sh -d /data/rawdata -b -Z

git clone https://github.com/petmri/ROCKETSHIP.git
cd ROCKETSHIP
python3 install.py

./rocketship_dce.sh          # DCE GUI
./rocketship_parametric.sh   # parametric T1 GUI

git clone https://github.com/petmri/AutoAIF.git && cd AutoAIF
python3 -m venv tf && source tf/bin/activate
pip install -r requirements.txt

# Download Pretrained weights, ~470 MB
curl -L -o model_weight_huber1.h5 \
  https://github.com/petmri/AutoAIF/releases/latest/download/model_weight_huber1.h5

python main_vif.py --mode inference \
  --input_path /path/to/dce.nii.gz \
  --model_weight_path model_weight_huber1.h5 \
  --save_output_path /path/to/output/

git clone https://github.com/petmri/AIFArtist.git && cd AIFArtist
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

python aif_artist.py /path/to/bids_dataset --rater AB

Contact and contributing

Questions are best raised as an issue on the relevant repository. For anything else, contact the lab maintainer at sabarnes@llu.edu. Pull requests are welcome on all repositories.

All repositories on GitHub