HCP Pipeline for Diffusion Processing
This is a Python implementation of the Diffusion Preprocessing procedure in
HCP Pipeline. Similar to the original
implementation, the input data need to be organised with HCP-like folder structure.
Installation
python3 -m pip isntall git+https://github.com/jadecci/hcp_pipeline_diffusion_py.git
Prerequisite
The pipeline requires 3 softwares: FSL, FreeSurfer, and Connectome Workbench. To use containerised
versions, the absolute path to the Singularity images can be passed with the --fsl_simg
,
--fs_simg
, and --wb_simg
flags respectively.
Usage
hcpdiffpy --help
usage: hcpdiffpy [-h] [--workdir WORK_DIR] [--output_dir OUTPUT_DIR] [--fsl_simg FSL_SIMG]
[--fs_simg FS_SIMG] [--wb_simg WB_SIMG] [--condordag]
subject_dir subject ndirs [ndirs ...] phases [phases ...] echo_spacing
HCP Pipeline for diffusion preprocessing
positional arguments:
subject_dir Absolute path to the subject's data folder (organised in HCP-like structure)
subject Subject ID
ndirs List of numbers of directions
phases List of 2 phase encoding directions
echo_spacing Echo spacing used for acquisition in ms
options:
-h, --help show this help message and exit
--workdir WORK_DIR Absolute path to work directory (default: /data/project/hcpa_dwi_proc)
--output_dir OUTPUT_DIR
Absolute path to output directory (default: /data/project/hcpa_dwi_proc)
--fsl_simg FSL_SIMG singularity image to use for command line functions from FSL (default: None)
--fs_simg FS_SIMG singularity image to use for command line functions from FreeSurfer
(default: None)
--wb_simg WB_SIMG singularity image to use for command line functions from Connectome
Workbench (default: None)
--condordag Submit workflow as DAG to HTCondor (default: False)
References
Glasser, M.F., et al. 2013. The minimal preprocessing pipelines for the Human Connectome Project.
NeuroImage, 80:105-24.
DOI: 10.1016/j.neuroimage.2013.04.127