BIDS-dMRIrecon

BIDS-dmrirecon is developed to perform dwi quantitative mapping/tractography based on CSD (constrained spherical deconvolution) estimation from MRtrix3. Main functions include:

• CSD estimation (dwi2fod)
• DTI mapping (dwi2tensor)
• Fiber tracking (https://github.com/MIC-DKFZ/TractSeg)
• DKI mapping (https://github.com/m-ama/PyDesigner)
• NODDI mapping (https://github.com/daducci/AMICO)
• ALPS mapping (https://github.com/gbarisano/alps)
• Network establishment (tck2connectome)
• Visualization (vtk/vtp files)

The input data should be arranged according to BIDS format. Input image modalities must include 3D-T1w and dMRI data.

BIDS-dMRIrecon 中文说明

Check details of brain atlases

Check bids-dmriprep version history in Change Log

Contents

• Install
• Before Running
• Running
• Input Argument
• Output Explanation

Install

install by pulling (recommend)

docker pull mindsgo-sz-docker.pkg.coding.net/neuroimage_analysis/base/bids-dmrirecon:latest
docker tag  mindsgo-sz-docker.pkg.coding.net/neuroimage_analysis/base/bids-dmrirecon:latest  bids-dmrirecon:latest

or install by docker build

cd BIDS-dmrirecon
docker build -t bids-dmrirecon:latest .

Before Running

use sMRIPrep for T1w data preprocessing

docker run -it --rm -v <bids_root>:/bids_dataset bids-smriprep:latest python /run.py /bids_dataset --participant_label 01 02 03 -MNInormalization -fsl_5ttgen -cleanup

use dMRIPrep for dMRI data preprocessing

docker run -it --rm --gpus all -v <bids_root>:/bids_dataset bids-dmriprep:latest python /run.py /bids_dataset /bids_dataset/derivatives/dmri_prep participant --participant_label 01 02 03 -mode complete

Running

default running

docker run -it --rm -v <bids_root>:/bids_dataset bids-dmrirecon:latest python /run.py /bids_dataset /bids_dataset/derivatives/dmri_recon participant --participant_label 01 02 03 -mode tract,dti_para,connectome -bundle_json /scripts/bundle_list_all72.json -wholebrain_fiber 5000000 -atlases AAL3_MNI desikan_T1w -cleanup

optional: summarize quantitative mapping metrics across all participants (post-hoc)

docker run -it --rm -v <bids_root>:/bids_dataset bids-dmrirecon:latest python /scripts/json2csv.py /bids_dataset participant

mode：

• -mode: Which type of dMRI analysis mode to run

• -mode tract: fiber tracking for predefined tracts (default)

• -mode dti_para: DTI parameter mapping, generating FA/MD/AD/RD.

• -mode alps: DTI-ALPS parameter mapping

• -mode dki_para: DKI parameter mapping, generating MK/RK/AK/KFA/MKT.

• -mode noddi_para: NODDI parameter mapping, generating ICVF/IVF/ODI.

• -mode connectome: structural network creation.

Input Argument

positional argument:

• /bids_dataset: The root folder of a BIDS valid dataset (sub-XX folders should be found at the top level in this folder).
• /bids_dataset/derivatives/dmri_recon: output path
• participant: process on participant level

optional argument:

• --participant_label [str]：A space delimited list of participant identifiers or a single identifier (the sub- prefix can be removed)
• --session_label [str]：A space delimited list of session identifiers or a single identifier (the ses- prefix can be removed)
• -tracking [prob|det]：tracking mode, probabilistic or deterministic. default = probabilistic
• -odf [tom|peaks]：orientation distribution function (ODF, the probability of diffusion in a given direction). tom refers to Tract Orientation Maps, default = tom.
• -wholebrain_fiber_num [int]：number of streamline for whole brain. default = 10000000
• -fiber_num [int]：number of streamline for each tract, default = 2000
• -bundle_json [str]：tract list to be tracked. default = /scripts/bundle_list.json
• -atlases [str]：A space delimited list of brain atlases. e.g. -atlases AAL3_MNI hcpmmp_T1w. FreeSurfer should be ran first for atlases ends with _T1w. See atlases/atlas_config_docker.json for details.
• -resume：resume running based on the temporary output generated by last run.
• -v：check version
• -cleanup: remove temporary files.

Output explanation

• DTI_mapping: DTI metrics, FA/MD/RD/AD/DEC-map(directionally-encoded colour map)
• fiber_tracts/bundle_segmentations: bundle mask
• fiber_tracts/Fibers: tck file for each tract
• fiber_tracts/fiber_streamline.json: summary for each tract
• DKI_mapping: DTI metrics, MK/RK/AK/KA
• NODDI_mapping: NODDI metrics, FIT_ICVF.nii.gz/FIT_OD.nii.gz/FIT_ISOVF.nii.gz/FIT_dir.nii.gz
• connectome: brain structural networks. see structural-connectome-metric-options for details.
• visualization: vtk/vtp files for each tract

Copyright

Copyright © [email protected] Please make sure that your usage of this code is in compliance with the code license.