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scfsl

FSL-based structural connectivity pipeline

Modified version of original pipeline to run as a sub-pipeline of our HPC pipelines with BIDS compatibility achieved with HeuDiConv. Preprocessing and Freesurfer parcellation added from fMRIPrep. MRIQC is run on HeuDiConv BIDS derivatives for quality control.

Prerequisites

This portion of the pipeline should be run after HeuDiConv, fmriprep, and QSIPrep preprocessing + reorient_fslstd recon have been run on the data.

The following examples use the CUDA 10.2 toolkit and runtime (loaded via module or native install)

Docker build

docker build -t scfsl_gpu:0.3.2 .

Or pull the image from mrfilbi/scfsl_gpu:0.3.2 (or newest tag)

Docker run command

docker run --gpus all -v /path/to/bids:/data mrfilbi/scfsl_gpu:0.3.2 /scripts/proc_fsl_connectome_fsonly.sh subject session

Singularity build

singularity build scfsl_gpu-v0.3.2.sif docker://mrfilbi/scfsl_gpu:0.3.2

Docker Example

# QSIPrep preprocessing + reorient to fsl
docker run --gpus all -v /path/project/bids:/datain \
-v /path/to/freesurfer/license.txt:/opt/freesurfer/license.txt \
pennbbl/qsiprep:0.15.1 /datain /datain/derivatives/ --recon-input /datain/derivatives/qsiprep/ \
--recon_spec reorient_fslstd \ --output-resolution 1.6 participant \
--participant-label sub-SUB330 --fs-license-file /opt/freesurfer/license.txt

# Running SCFSL GPU tractography
 docker exec --gpus all -e LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.2/lib64 \
  -v /path/to/freesurfer/license.txt:/opt/freesurfer/license.txt \
  -v /path/project/bids:/data mrfilbi/scfsl_gpu:0.3.2 /bin/bash /scripts/proc_fsl_connectome_fsonly.sh sub-SUB339 ses-A

You may need to set your CUDA toolkit version to 9.1 and set the environmental variable for LD_LIBRARY_PATH to run successfully

Singularity Example

# QSIPrep preprocessing + reorient to fsl
singularity run --nv -B /path/project/bids:/datain,/path/to/freesurfer/license.txt:/opt/freesurfer/license.txt \
/path/to/qsiprep-v0.15.1.sif /datain /datain/derivatives/ --recon-input /datain/derivatives/qsiprep/ --recon_spec reorient_fslstd \ --output-resolution 1.6 participant --participant-label sub-SUB330 --fs-license-file /opt/freesurfer/license.txt

# Running SCFSL GPU tractography
SINGULARITY_ENVLD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.2/lib64 \
singularity exec --nv -B /path/to/freesurfer/license.txt:/opt/freesurfer/license.txt,/path/project/bids:/data \
/path/to/scfsl_gpu-v0.3.2.sif /bin/bash /scripts/proc_fsl_connectome_fsonly.sh sub-SUB339 ses-A

Outputs

In addition to the fdt_network_matrix produced by probtrackx2 for the masks derived from Freesurfer parcellation (generated in sMRIPrep/fMRIPrep), this sub-pipeline also outputs node-labeled csv files of the NxN streamline-weighted and ROI volume-weighted structural connectome.

Performance

From initial testing (on 23 datasets from 7T 1.6mm isotropic CMRR DWI):

OS (host) CUDA Version GPU(s) CPU(s) RAM Run time
CentOS 9.1 Nvidia Tesla V100 16GB Intel Xeon Gold 6138 2.00GHz (80 threads) 192GB 30-35 minutes

Peak GPU memory usage: 14435MiB / 16160MiB

From testing 30 datasets from 3T 2.0mm isotropic CMRR DWI):

OS (host) CUDA Version GPU(s) CPU(s) RAM Run time
CentOS 9.1 Nvidia Tesla V100 16GB Intel Xeon Gold 6138 2.00GHz (80 threads) 192GB 25-30 minutes
CentOS 10.2 Nvidia Tesla V100 16GB Intel Xeon Gold 6138 2.00GHz (80 threads) 192GB 25-30 minutes

Peak GPU memory usage: 13999MiB / 16160MiB