This tool provides a simple and robust command-line interface for performing voxel-wise Patlak analysis on dynamic PET data. It is designed to be easy to install and use, making quantitative PET analysis more accessible for researchers.

• DICOM to NIfTI Conversion: Automatically handles the conversion of dynamic PET DICOM series to a single 4D NIfTI file using the dcm2niix tool.
• Patient-Specific Input Function: Scales a population-based input function (PBIF) with a patient-specific Arterial Input Function (AIF) mean extracted from a provided mask.
• Voxel-wise Patlak Plot: Computes Ki (uptake rate constant) and Vd (distribution volume) maps by performing a linear regression on the Patlak plot for each voxel.
• NIfTI Output: Saves the resulting Ki and Vd maps as standard NIfTI files, compatible with most medical imaging software.

To get started, you'll need Python 3.8+ and the dcm2niix executable installed on your system.

Create a virtual environment and activate it (recommended):

Windows:

python -m venv mypatlak_env
mypatlak_env\Scripts\activate

Linux/Mac:

python -m venv mypatlak_env
source mypatlak_env/bin/activate  

Simply use pip to install the package from PyPI:

pip install .

The pypatlak command-line tool is designed for ease of use.

pypatlak <dicom_dir> <aif_mask_path> <output_dir> <patient_id> [options]

Arguments

<dicom_dir>: Path to the directory containing the dynamic PET DICOM files.

<aif_mask_path>: Path to the NIfTI file (.nii or .nii.gz) for the AIF mask.

<output_dir>: Path to the directory where the output maps will be saved.

<patient_id>: A unique identifier for the patient, used for naming output files.

Options

--rf RECOVERY_FACTOR: Correct for the partial volume effect and scale AIF mask mean values (default: 1.0)

--input-function INPUT_FUNCTION: Path to the population-based input function CSV file. (default: ./input_function.csv)

--dcm2niix-path DCM2NIIX_PATH: Path to the dcm2niix executable. (default: dcm2niix, assumes it's in your system's PATH)

Example

To run the analysis on a dataset:

pypatlak /path/to/my/dicoms /path/to/my/aif_mask.nii.gz /path/to/my/dicoms/results my_patient_01 --rf 1.2

To run the analysis, your input data should be organized as follows:

.
├── patient_data/
│   ├── patient_1/
│   │   ├── pt01_DICOM/  # Directory containing DICOM files
│   │   └── pt01_aif.nii.gz # AIF mask file
│   └── patient_2/
│       ├── pt02_DICOM/
│       └── pt02_aif.nii.gz
├── input_function.csv   # The population-based input function
└── ...

After the analysis is complete, the specified output directory will contain the generated parametric maps:

results/
├── Ki_map.nii
└── Vd_map.nii

If you use this software in your research, please cite the following repository and/or paper:

Repository Citation:

BibTeX:

@software{AArtesani_PyPatlak_2025,
  author = {Alessia Artesani},
  title = {{PyPatlak: A Command-Line Tool For Patlak Analysis}},
  url = {https://github.com/alessiaartesani/pypatlak},
  year = {2025},
}

Paper Citation:

pyPatlak: Open-Source Voxel-Wise Patlak Analysis of Dynamic PET Data Alessia Artesani medRxiv 2025.09.16.25335861; doi: https://doi.org/10.1101/2025.09.16.25335861

License

This project is licensed under the MIT License.

Acknowledgements

This tool uses the nibabel, pydicom, numpy, and scipy libraries. The DICOM to NIfTI conversion is powered by dcm2niix.

Contributors

Alessia Artesani - Initial development and project setup.