This BIDS App provides a pipeline for preprocessing and head motion correction of Positron Emission Tomography (PET) data following the Brain Imaging Data Structure (BIDS) standard.
- BIDS compliant PET data input and output
- Rigid body head motion correction using robust registration
- Motion parameter estimation for further analysis
- Integration with BIDS Derivatives for compatibility with other BIDS Apps
- Compatible with Python 3
- Python 3.9+
- Nipype
- NiBabel
- FSL
- FreeSurfer
Clone the repository and install the required packages:
git clone https://github.com/mnoergaard/petprep_hmc.git cd petprep_hmc pip install -e .
The package is also pip installable and can be installed using the following command
pip install petprep-hmc
To run the PETPrep Head Motion Correction BIDS App, use the following command:
python3 run.py --bids_dir /path/to/bids_input --output_dir /path/to/bids_output --analysis_level participant [--participant_label PARTICIPANT_LABEL]
--bids_dir: Path to the input BIDS dataset--output_dir: Path to the output directory for preprocessed data--analysis_level: Level of the analysis that will be performed. Multiple participant level analyses can be run independently (in parallel) using the same output_dir.--participant_label: (Optional) A single participant label or a space-separated list of participant labels to process. If not provided, all participants in the dataset will be processed.--mc_start_time: (Optional) Start time for when to perform motion correction (subsequent frame will be chosen) in seconds (default = 120 seconds).--mc_fwhm: (Optional) FWHM for smoothing of frames prior to estimating motion (default = 10mm).--mc_thresh: (Optional) Threshold below the following percentage (0-100) of framewise ROBUST RANGE prior to estimating motion correction (default = 20).--n_procs: (Optional) Number of processors allocated to be used when running the workflow.--no_resample: (Optional) Whether or not to resample the motion corrected PET data to lowest x/y/z dim in original data (default = False).--skip_bids_validator: (Optional) Whether or not to perform BIDS dataset validation.
For example, to process participant sub-01, use the following command:
python3 run.py --bids_dir /data/bids_input --output_dir /data/bids_output --participant_label 01
Preprocessed PET data along with the estimated motion parameters (confounds) and motion plots will be stored in the directory specified by --output_dir or, if no output directory is specified, in <bids_dir>/derivatives/petprep_hmc following the BIDS Derivatives standard.
To run petprep_hmc workflow using Docker, you must first have Docker installed on your system. You can download and install Docker from https://www.docker.com/.
Once you have Docker installed, you can pull the petprep_hmc Docker image from Docker Hub by running the following command:
docker pull martinnoergaard/petprep_hmc:latest
To run the petprep_hmc Docker container, use the following command:
docker run -it --rm \
-v /path/to/bids_input:/data/input \
-v /path/to/bids_output:/data/output \
-v /path/to/freesurfer_license:/opt/freesurfer/license.txt \
martinnoergaard/petprep_hmc:latest \
--bids_dir /data/input --output_dir /data/output --analysis_level participant [--participant_label PARTICIPANT_LABEL]
This command mounts your local input and output directories, as well as the FreeSurfer license file, to the Docker container. The petprep_hmc script is then executed within the container, processing the input data and saving the results to the specified output directory.
For questions or bug reports, please open an issue on the GitHub repository.
This BIDS App is released under the Apache 2.0 license. Please see the Apache 2.0 license file for more details.