Arterial Spin Labelling

arterial spin labelling
blood flow
Measuring cerebral blood flow
Author

Alex Weber

Published

March 11, 2025

Modified

March 11, 2025

Arterial Spin Labelling

Arterial Spin Labelling can be used to calculate the cerebral blood flow (CBF) in the brain: "perfusion MRI permits noninvasive quantification of blood flow, which is an important physiological parameter"

Essentially it 'tags' arterial blood and then measures it in the brain as it perfuses through (very neat)

This is a good resource:
www.ncbi.nlm.nih.gov/pmc/articles/PMC4190138/

As well as this:
mri-q.com/quantifying-flow.html

ASL Prep

similar to fmriprep or qsiprep if you are familiar

ASLPrep is a Arterial Spin Labeling (ASL) data preprocessing and Cerebral Blood FLow (CBF) computation pipeline that is designed to provide an easily accessible, state-of-the-art interface that is robust to variations in scan acquisition protocols and that requires minimal user input, while providing easily interpretable and comprehensive error and output reporting. It performs basic processing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skullstripping etc.), CBF computation, denoising CBF, CBF partial volume correction and providing outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state CBF, graph theory measures, surface or volume-based statistics, etc.

The ASLPrep pipeline uses a combination of tools from well-known software packages, including FSL, ANTs, FreeSurfer and AFNI . This pipeline was designed to provide the best software implementation for each state of preprocessing, and will be updated as newer and better neuroimaging software become available.

This tool allows you to easily do the following:

  • Take ASL data from raw to fully preprocessed form.
  • Compute Cerebral Blood Flow(CBF), denoising and partial volume correction
  • Implement tools from different software packages.
  • Achieve optimal data processing quality by using the best tools available.
  • Receive verbose output concerning the stage of preprocessing for each subject, including meaningful errors.
  • Automate and parallelize processing steps, which provides a significant speed-up from typical linear, manual processing.

Install w Docker

It is recommended to use a docker or singularity

docker pull pennlinc/aslprep:latest

Run w Docker

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

Explanation:

docker run -ti -m 12GB --rm                         # attach to the container interactively
-v $HOME/license.txt:/license/license.txt           # mount the freesurfer license directory
-v $HOME/ds000240:/data:ro                          # mount the data directory to the container directory
-v $HOME/ds000240-results:/out:rw                   # mount the output directory to the container directory
-v $HOME/tmp/ds000240-workdir:/work                 # mount working directory
pennlinc/aslprep                                    # the container name: aslprep
/data                                               # the data directory
/out/aslprep                                        # the output directory
participant                                         # analysis type: participant
--participant-label 01                              # select participant 01
--fs-license-file /license/license.txt              # setting freesurfer license file
-w /work                                            # setting working directory

Command-Line Arguments:

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}

Instructions:

Instructions I wrote: instructions

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