Usage Notes
Execution and the BIDS format
The main input to ASLPREP is the path of the dataset that needs processing.
Note
We have created a simple tool for converting ASL data into currently valid BIDS, available at PennLINC. Note that some of the parameters necessary for running ASLPrep cannot be extracted directly from the DICOM header — to obtain these parameters, we recommend consulting the MRI physicist/technician at the scanner. The conversion of ASL into BIDS on Flywheel using fw-heudiconv is described here at fw-heudiconv.
The input dataset is required to be in valid BIDS 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.
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
ASLPrep: ASL PREProcessing workflows v0.3.0
usage: aslprep [-h] [--version] [--skip_bids_validation]
[--participant-label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
[--echo-idx ECHO_IDX] [--bids-filter-file FILE]
[--anat-derivatives PATH] [--nprocs NPROCS]
[--omp-nthreads OMP_NTHREADS] [--mem MEMORY_GB] [--low-mem]
[--use-plugin FILE] [--anat-only] [--boilerplate_only]
[--md-only-boilerplate] [-v]
[--ignore {fieldmaps,slicetiming,sbref} [{fieldmaps,slicetiming,sbref} ...]]
[--longitudinal]
[--output-spaces [OUTPUT_SPACES [OUTPUT_SPACES ...]]]
[--asl2t1w-init {register,header}] [--asl2t1w-dof {6,9,12}]
[--force-bbr] [--force-no-bbr] [--m0_scale M0_SCALE]
[--random-seed RANDOM_SEED] [--dummy-vols DUMMY_VOLS]
[--smooth_kernel SMOOTH_KERNEL] [--scorescrub] [--basil]
[--skull-strip-template SKULL_STRIP_TEMPLATE]
[--skull-strip-fixed-seed]
[--skull-strip-t1w {auto,skip,force}] [--fmap-bspline]
[--fmap-no-demean] [--use-syn-sdc] [--force-syn]
[--fs-license-file FILE] [-w WORK_DIR] [--clean-workdir]
[--resource-monitor] [--reports-only] [--run-uuid RUN_UUID]
[--write-graph] [--stop-on-first-crash] [--notrack] [--sloppy]
bids_dir output_dir {participant}
Positional Arguments
- bids_dir
the root folder of a BIDS valid dataset (sub-XXXXX folders should be found at the top level in this folder).
- output_dir
the output path for the outcomes of preprocessing and visual reports
- analysis_level
Possible choices: participant
processing stage to be run, only “participant” in the case of ASLPREP (see BIDS-Apps specification).
Named Arguments
- --version
show program’s version number and exit
Options for filtering BIDS queries
- --skip_bids_validation, --skip-bids-validation
assume the input dataset is BIDS compliant and skip the validation
- --participant-label, --participant_label
a space delimited list of participant identifiers or a single identifier (the sub- prefix can be removed)
- --echo-idx
select a specific echo to be processed in a multiecho series
- --bids-filter-file
a JSON file describing custom BIDS input filters using PyBIDS. For further details, please check out https://aslprep.readthedocs.io/en/0.3.0/faq.html#how-do-I-select-only-certain-files-to-be-input-to-ASLPrep
- --anat-derivatives
Reuse the anatomical derivatives from another ASLPrep run or calculated with an alternative processing tool (NOT RECOMMENDED).
Options to handle performance
- --nprocs, --nthreads, --n_cpus, --n-cpus
maximum number of threads across all processes
- --omp-nthreads
maximum number of threads per-process
- --mem, --mem_mb, --mem-mb
upper bound memory limit for ASLPrep processes
- --low-mem
attempt to reduce memory usage (will increase disk usage in working directory)
- --use-plugin, --nipype-plugin-file
nipype plugin configuration file
- --anat-only
run anatomical workflows only
- --boilerplate_only
generate boilerplate only
- --md-only-boilerplate
skip generation of HTML and LaTeX formatted citation with pandoc
- -v, --verbose
increases log verbosity for each occurence, debug level is -vvv
Workflow configuration
- --ignore
Possible choices: fieldmaps, slicetiming, sbref
ignore selected aspects of the input dataset to disable corresponding parts of the workflow (a space delimited list)
- --longitudinal
treat dataset as longitudinal - may increase runtime
- --output-spaces
Standard and non-standard spaces to resample anatomical and functional images to. Standard spaces may be specified by the form
<SPACE>[:cohort-<label>][:res-<resolution>][...]
, where<SPACE>
is a keyword designating a spatial reference, and may be followed by optional, colon-separated parameters. Non-standard spaces imply specific orientations and sampling grids. Important to note, theres-*
modifier does not define the resolution used for the spatial normalization. To generate no ASL outputs, use this option without specifying any spatial references.- --asl2t1w-init
Possible choices: register, header
Either “register” (the default) to initialize volumes at center or “header” to use the header information when coregistering ASL to T1w images.
- --asl2t1w-dof
Possible choices: 6, 9, 12
Degrees of freedom when registering ASL to T1w images. 6 degrees (rotation and translation) are used by default.
- --force-bbr
Always use boundary-based registration (no goodness-of-fit checks)
- --force-no-bbr
Do not use boundary-based registration (no goodness-of-fit checks)
- --m0_scale
relative scale between asl and M0.
- --random-seed
Initialize the random seed for the workflow
- --dummy-vols
Number of initial volumes to ignore
- --smooth_kernel
Smoothing kernel for the M0 image(s)
- --scorescrub
Sudipto algoritms for denoising CBF
- --basil
FSL’s CBF computation with spatial regularization and partial volume correction
Specific options for ANTs registrations
- --skull-strip-template
select a template for skull-stripping with antsBrainExtraction
- --skull-strip-fixed-seed
do not use a random seed for skull-stripping - will ensure run-to-run replicability when used with –omp-nthreads 1 and matching –random-seed <int>
- --skull-strip-t1w
Possible choices: auto, skip, force
determiner for T1-weighted skull stripping (‘force’ ensures skull stripping, ‘skip’ ignores skull stripping, and ‘auto’ applies brain extraction based on the outcome of a heuristic to check whether the brain is already masked).
Specific options for handling fieldmaps
- --fmap-bspline
fit a B-Spline field using least-squares (experimental)
- --fmap-no-demean
do not remove median (within mask) from fieldmap
Specific options for SyN distortion correction
- --use-syn-sdc
EXPERIMENTAL: Use fieldmap-free distortion correction
- --force-syn
EXPERIMENTAL/TEMPORARY: Use SyN correction in addition to fieldmap correction, if available
Specific options for FreeSurfer preprocessing
- --fs-license-file
Path to FreeSurfer license key file. Get it (for free) by registering at https://surfer.nmr.mgh.harvard.edu/registration.html
Other options
- -w, --work-dir
path where intermediate results should be stored
- --clean-workdir
Clears working directory of contents. Use of this flag is notrecommended when running concurrent processes of aslprep.
- --resource-monitor
enable Nipype’s resource monitoring to keep track of memory and CPU usage
- --reports-only
only generate reports, don’t run workflows. This will only rerun report aggregation, not reportlet generation for specific nodes.
- --run-uuid
Specify UUID of previous run, to include error logs in report. No effect without –reports-only.
- --write-graph
Write workflow graph.
- --stop-on-first-crash
Force stopping on first crash, even if a work directory was specified.
- --notrack
Opt-out of sending tracking information of this run to the aslprep developers. This information helps to improve aslprep and provides an indicator of real world usage crucial for obtaining funding.
- --sloppy
Use low-quality tools for speed - TESTING ONLY
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.