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Usage Notes

Execution and the BIDS format

The main input to ASLPREP is the path of the dataset that needs processing.

The input dataset is required to be in valid BIDS (Brain Imaging Data Structure) format, and it must include at least one T1w structural image. We highly recommend that you validate your dataset with the free, online BIDS Validator.

Important

Please note that ASL data in BIDS datasets should already be scaled <https://bids-specification.readthedocs.io/en/v1.8.0/ 04-modality-specific-files/01-magnetic-resonance-imaging-data.html#scaling>.

What this means is that the M0 scans in your dataset should preferably be scaled before running ASLPrep.

Please see the BIDS starter kit for information about converting ASL data to BIDS.

If your data are not already scaled, you should use the --m0_scale parameter when running ASLPrep.

The exact command to run ASLPrep depends on the installation method. The common parts of the command follow the BIDS-Apps definition. For example: :

aslprep data/bids_root/ out/ participant -w work/

Command-Line Arguments

Running ASLPrep via Docker containers

For every new version of ASLPrep that is released, a corresponding Docker image is generated.

In order to run ASLPrep Docker images, the Docker Engine must be installed.

If you have used ASLPrep via Docker in the past, you might need to pull down a more recent version of the image:

$ docker pull pennlinc/aslprep:<latest-version>

ASLPrep can be run interacting directly with the Docker Engine via the docker run command line, or through a lightweight wrapper that was created for convenience.

Running ASLPrep directly interacting with the Docker Engine

Running containers as a user.

In order to run docker smoothly, it is best to prevent permissions issues associated with the root file system. Running docker as user on the host is to ensure the ownership of files written during the container execution.

ASLPrep requires a significant amount of memory, typicaly around 12GB per subject. If using docker desktop, you can set this in preferences. You can also set it on the command line.

A docker container can be created using the following command:

$ docker run -ti -m 12GB --rm \
    -v path/to/data:/data:ro \
    -v path/to/output:/out \
    pennlinc/aslprep:<latest-version> \
    /data /out/out \
    participant

For example:

$ docker run -ti -m 12GB --rm \
    -v $HOME/ds000240:/data:ro \
    -v $HOME/ds000240-results:/out:rw \
    -v $HOME/tmp/ds000240-workdir:/work \
    -v ${FREESURFER_HOME}:/fs \
    pennlinc/aslprep:<latest-version> \
    /data /out/aslprep-<latest-version> \
    participant \
    --participant-label '01'
    --fs-license-file ${FREESURFER_HOME}/license.txt
    -w /work

See usage for more information.

Running ASLPrep via Singularity containers

Preparing a Singularity Image

Singularity version >= 2.5: If the version of Singularity installed on your HPC (High-Performance Computing) system is modern enough you can create a Singularity image directly on the system using the following command: :

$ singularity build aslprep-<version>.simg docker://pennlinc/aslprep:<version>

where <version> should be replaced with the desired version of ASLPrep that you want to download.

Running a Singularity Image

If the data to be preprocessed is also on the HPC or a personal computer, you are ready to run ASLPrep. :

$ singularity run --cleanenv aslprep.simg \
    path/to/data/dir path/to/output/dir \
    participant \
    --participant-label label

Handling environment variables

By default, Singularity interacts with all environment variables from the host. The host libraries could accidentally conflict with singularity variables. To avoid such a situation, it is recommended that you sue the --cleanenv or -e flag. For instance: :

$ singularity run --cleanenv aslprep.simg \
    /work/789/asdf/ $WORK/output \
    participant \
    --participant-label 01

Relevant aspects of the $HOME directory within the container. By default, Singularity will bind the user's $HOME directory on the host into the /home/$USER directory (or equivalent) in the container. Most of the times, it will also redefine the $HOME environment variable and update it to point to the corresponding mount point in /home/$USER. However, these defaults can be overwritten in your system. It is recommended that you check your settings with your system's administrators. If your Singularity installation allows it, you can work around the $HOME specification combining the bind mounts argument (-B) with the home overwrite argument (--home) as follows: :

$ singularity run -B $HOME:/home/aslprep --home /home/aslprep \
    --cleanenv aslprep.simg <aslprep arguments>

The FreeSurfer license

ASLPRep uses FreeSurfer tools, which require a license to run.

To obtain a FreeSurfer license, simply register for free at https://surfer.nmr.mgh.harvard.edu/registration.html.

When using manually-prepared environments or singularity, FreeSurfer will search for a license key file first using the $FS_LICENSE environment variable and then in the default path to the license key file ($FREESURFER_HOME/license.txt). If using the --cleanenv flag and $FS_LICENSE is set, use --fs-license-file $FS_LICENSE to pass the license file location to ASLPrep.

It is possible to run the docker container pointing the image to a local path where a valid license file is stored. For example, if the license is stored in the $HOME/.licenses/freesurfer/license.txt file on the host system: :

$ docker run -ti --rm \
    -v $HOME/fullds005:/data:ro \
    -v $HOME/dockerout:/out \
    -v $HOME/.licenses/freesurfer/license.txt:/opt/freesurfer/license.txt \
    pennlinc/aslprep:latest \
    /data /out/out \
    participant \
    --ignore fieldmaps

Troubleshooting

Logs and crashfiles are written to the <output dir>/aslprep/sub-<participant_label>/log directory. Information on how to customize and understand these files can be found on the nipype debugging page.

Support and communication

The documentation of this project is found here: https://aslprep.readthedocs.io.

All bugs, concerns and enhancement requests for this software can be submitted here: https://github.com/PennLINC/aslprep/issues.

If you have a question about using ASLPrep, please create a new topic on NeuroStars with the "Software Support" category and the "aslprep" tag. The ASLPrep developers follow NeuroStars, and will be able to answer your question there.