Where this fits¶

ROCKETSHIP covers steps 5 and 6. The full DCEasy pipeline:

Explore the DCEasy family →

ROCKETSHIP is an open source toolbox for the processing and analysis of dynamic contrast-enhanced (DCE) and parametric MRI data. It provides quantitative pharmacokinetic modeling, parametric mapping of T₁, T₂, T₂^* and the apparent diffusion coefficient, and the supporting workflow required to take a study from acquired images to parameter maps.

It was developed at the Biological Imaging Center at the California Institute of Technology and at Loma Linda University, and is used in clinical and preclinical imaging research worldwide.

DCEasy¶

ROCKETSHIP is part of the DCEasy family of software for quantitative dynamic contrast-enhanced MRI. DCEasy provides an integrated set of tools covering the full path from scanner output to quantitative results:

• DICOM to BIDS conversion, preparing acquired studies into a standard organized layout with the acquisition metadata that quantitative analysis requires.
• Automatic and manual arterial input function selection, addressing the largest single source of systematic error in quantitative DCE-MRI.
• Batch processing, applying a validated analysis consistently across entire studies rather than one session at a time.
• Pharmacokinetic modeling and parametric mapping, provided by ROCKETSHIP itself.

The components are designed to work together, so a study prepared with the DCEasy conversion and input function tools can be processed by ROCKETSHIP without further preparation. Each is also usable independently. See dceasy.org for a complete list of available tools.

Citation¶

If you use ROCKETSHIP in your work, please cite:

Ng, T.S.C., et al. ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies. BMC Medical Imaging, 15, 19 (2015). PMID: 26076957

Start here¶

• Python Walkthrough — the recommended interface for new work
• MATLAB DCE Walkthrough
• MATLAB DSC Walkthrough
• MATLAB Parametric Walkthrough
• GPU and CPU Acceleration

Technical reference:

• DCE Options — every configuration option
• Signal to Concentration — the conversion math and its assumptions
• Pharmacokinetic Models — equations, parameters and model selection

Python quick start¶

The Python implementation is the recommended interface and is under active development.

git clone https://github.com/petmri/ROCKETSHIP.git

cd ROCKETSHIP && python3 install.py

Launch the GUI:

./rocketship_dce.sh

Or work from the command line:

source .venv/bin/activate

python run_parametric_python_cli.py

python run_dce_python_cli.py

Parametric T₁ mapping comes first, since DCE analysis requires a pre-contrast T₁ map as input. Graphical interfaces are available for both steps through run_dce_python_gui.py and run_parametric_python_gui.py.

See the Python Walkthrough for the full procedure.

MATLAB quick start (legacy)¶

The MATLAB implementation is the original version and will be maintained, but new features will only be implemented in python.

git clone https://github.com/petmri/ROCKETSHIP.git

1. Add the ROCKETSHIP folder to your MATLAB path.
2. Calculate T₁ maps with run_parametric.m.
3. Check the T₁ maps with run_analysis.m.
4. Calculate DCE maps with run_dce.m.

Selected publications¶

A more complete list is available on Google Scholar.

• Pan, H., et al. Liganded magnetic nanoparticles for magnetic resonance imaging of α-synuclein. npj Parkinson's Disease, 11(1), 88 (2025). PMID: 40268938
• Llull, B., et al. Blood-Brain Barrier Disruption Predicts Poor Outcome in Subarachnoid Hemorrhage: A Dynamic Contrast-Enhanced MRI Study. Stroke, 56(9), 2633-2643 (2025). PMID: 40557536
• Reas, E.T., et al. APOE ε4-related blood-brain barrier breakdown is associated with microstructural abnormalities. Alzheimer's & Dementia, 20(12), 8615-8624 (2024). PMID: 39411970
• Montagne, A., et al. APOE4 leads to blood-brain barrier dysfunction predicting cognitive decline. Nature, 581(7806), 71-76 (2020). PMID: 32376954
• Backhaus, P., et al. Toward precise arterial input functions derived from DCE-MRI through a novel extracorporeal circulation approach in mice. Magnetic Resonance in Medicine, 84(3), 1404-1415 (2020). PMID: 32077523
• Bagley, S.J., et al. Clinical Utility of Plasma Cell-Free DNA in Adult Patients with Newly Diagnosed Glioblastoma: A Pilot Prospective Study. Clinical Cancer Research, 26(2), 397-407 (2020). PMID: 31666247
• Ng, T.S.C., et al. Clinical Implementation of a Free-Breathing, Motion-Robust Dynamic Contrast-Enhanced MRI Protocol to Evaluate Pleural Tumors. AJR American Journal of Roentgenology, 215(1), 94-104 (2020). PMID: 32348181
• Pacia, C.P., et al. Feasibility and safety of focused ultrasound-enabled liquid biopsy in the brain of a porcine model. Scientific Reports, 10(1), 7449 (2020). PMID: 32366915
• Boehm-Sturm, P., et al. Low-Molecular-Weight Iron Chelates May Be an Alternative to Gadolinium-based Contrast Agents for T1-weighted Contrast-enhanced MR Imaging. Radiology, 286(2), 537-546 (2018). PMID: 28880786
• Sta Maria, N.S., et al. Low Dose Focused Ultrasound Induces Enhanced Tumor Accumulation of Natural Killer Cells. PLOS ONE, 10(11), e0142767 (2015). PMID: 26556731

Support¶

Questions not answered by this documentation may be directed to Sam Barnes at sabarnes@llu.edu. Bug reports and feature requests are best raised as issues on GitHub.

License¶

ROCKETSHIP is released under the terms described in the LICENSE file.