aslprep

Preprocessing of arterial spin labeling (ASL) data

https://github.com/pennlinc/aslprep

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Preprocessing of arterial spin labeling (ASL) data

README.rst

#######################################################
*ASLPrep*: A Robust Preprocessing Pipeline for ASL Data
#######################################################

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This pipeline is developed by the `Satterthwaite lab at the University of Pennsylvania
`_ for use at the `The Lifespan Informatics and Neuroimaging Center
at the University of Pennsylvania `_, as well as for
open-source software distribution.

*****
About
*****

.. image:: https://raw.githubusercontent.com/PennLINC/aslprep/main/docs/_static/aslprepworkflow.png

*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.

More information and documentation can be found at https://aslprep.readthedocs.io/.

*******
ASLPrep
*******

*ASLPrep* adapts the preprocessing steps depending on the input dataset
and provide results as good as possible independently of scanner make and scanning parameters
With the BIDS input, little or no parameters are required allowing ease of operation.
*ASLPrep* also provides visual reports for each subject,
detailing the most important processing steps.

****************
Acknowledgements
****************

Please acknowledge this work using the citation boilerplate that *ASLPrep* includes
in the visual report generated for every subject processed.

************************************************
On the relationship between ASLPrep and fMRIPrep
************************************************

ASLPrep is largely based on fMRIPrep, as ASL processing is very similar to fMRI processing.
fMRIPrep is developed by a larger group and receives more regular feedback on its workflow,
so we (the ASLPrep developers) try to update ASLPrep in line with fMRIPrep.

ASLPrep and fMRIPrep are both part of the NiPreps community, but ASLPrep is in a unique situation
because it is owned and maintained by Ted Satterthwaite and his team.
As such, while we do our best to keep ASLPrep in sync with fMRIPrep, differences may arise
due to choices by leadership or, more commonly, necessarily differential processing for different
modalities.

There are several crucial differences between ASL and fMRI processing,
which must be accounted for in ASLPrep:

1. The ASL reference image is selected from the highest-contrast volume type within the time series,
   rather than from the first N volumes.
2. ASL processing does not include slice timing correction.
   Instead, post-labeling delays are shifted based on the slice timing when CBF is calculated.
3. While ASL motion correction may use the same algorithm as fMRI motion correction,
   different volume types in the ASL time series exhibit different contrasts, which can introduce
   artifacts into the motion-corrected data.
   As such, ASLPrep motion corrects each volume type separately,
   and then concatenates the corrected time series back together.
4. ASLPrep includes extra steps to do the following:
   1. Calculate CBF.
   2. Calculate CBF QC metrics, paralleling the ASL confound calculation.
   3. Plot CBF results, paralleling ASL plots.
   4. Parcellate CBF results with a range of atlases.
5. fMRIPrep contains a lot of code to handle multi-echo fMRI.
   While multi-echo ASL does exist, it is very rare, so we do not include any multi-echo-specific
   elements in ASLPrep at the moment.

Owner

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  ASLPrep: A Robust Preprocessing Pipeline for ASL Data
message: >-
  If you use this software, please cite it using the
  metadata from this file, as well as the Nature Methods paper
  (doi:10.1038/s41592-022-01458-7).
type: software
authors:
  - given-names: Taylor
    family-names: Salo
    email: salot@pennmedicine.upenn.edu
    affiliation: University of Pennsylvania
    orcid: 'https://orcid.org/0000-0001-9813-3167'
  - given-names: Azeez
    family-names: Adebimpe
    affiliation: University of Pennsylvania
    orcid: 'https://orcid.org/0000-0001-9049-0135'
  - given-names: Maxwell
    family-names: Bertolero
    orcid: 'https://orcid.org/0000-0002-2691-3698'
    affiliation: University of Pennsylvania
  - given-names: Sudipto
    family-names: Dolui
    orcid: 'https://orcid.org/0000-0002-9035-3795'
  - given-names: Matthew
    family-names: Cieslak
    email: matthew.cieslak@pennmedicine.upenn.edu
    affiliation: University of Pennsylvania
    orcid: 'https://orcid.org/0000-0002-1931-4734'
  - given-names: Kristin
    family-names: Murtha
    affiliation: University of Pennsylvania
    orcid: 'https://orcid.org/0009-0006-5820-6740'
  - given-names: Erica
    family-names: Baller
    orcid: 'https://orcid.org/0000-0002-7987-3773'
  - given-names: Bradley
    family-names: Boeve
    orcid: 'https://orcid.org/0000-0002-4153-8187'
  - given-names: Adam
    family-names: Boxer
    orcid: 'https://orcid.org/0000-0002-1215-5064'
  - given-names: Ellyn R.
    family-names: Butler
    orcid: 'https://orcid.org/0000-0001-6316-6444'
  - given-names: Phil
    family-names: Cook
  - given-names: Stan
    family-names: Colcombe
    orcid: 'https://orcid.org/0000-0001-5051-2491'
  - given-names: Sydney
    family-names: Covitz
    affiliation: University of Pennsylvania
    orcid: 'https://orcid.org/0000-0002-7430-4125'
  - given-names: Christos
    family-names: Davatzikos
    orcid: 'https://orcid.org/0000-0002-1025-8561'
  - given-names: Diego G.
    family-names: Davila
  - given-names: Mark A.
    family-names: Elliott
    orcid: 'https://orcid.org/0000-0003-3807-2723'
  - given-names: Matthew W.
    family-names: Flounders
    orcid: 'https://orcid.org/0000-0001-7014-4665'
  - given-names: Alexandre R.
    family-names: Franco
  - given-names: Mathias
    family-names: Goncalves
    affiliation: Stanford University
    orcid: 'https://orcid.org/0000-0002-7252-7771'
  - given-names: Raquel E.
    family-names: Gur
    orcid: 'https://orcid.org/0000-0002-4082-8502'
  - given-names: Ruben C.
    family-names: Gur
    orcid: 'https://orcid.org/0000-0002-9657-1996'
  - given-names: Basma
    family-names: Jaber
  - given-names: Christopher J.
    family-names: Markiewicz
    affiliation: Stanford University
    orcid: 'https://orcid.org/0000-0002-6533-164X'
  - given-names: Corey
    family-names: McMillian
  - name: "The ALLFTD Consortium"
  - given-names: Michael
    family-names: Milham
    orcid: 'https://orcid.org/0000-0003-3532-1210'
  - given-names: Henk J. M. M.
    family-names: Mutsaerts
    orcid: 'https://orcid.org/0000-0003-0894-0307'
  - given-names: Desmond J.
    family-names: Oathes
    orcid: 'https://orcid.org/0000-0001-7346-2669'
  - given-names: Christopher A.
    family-names: Olm
    orcid: 'https://orcid.org/0000-0002-4858-2359'
  - given-names: Jeffrey S.
    family-names: Phillips
  - given-names: Ashley
    family-names: Ptinis
  - given-names: Will
    family-names: Tackett
  - given-names: David R.
    family-names: Roalf
    orcid: 'https://orcid.org/0000-0002-1728-9782'
  - given-names: Howard
    family-names: Rosen
    orcid: 'https://orcid.org/0000-0001-9281-7402'
  - given-names: Brooke
    family-names: Sevchik
    orcid: 'https://orcid.org/0009-0005-2187-2275'
  - given-names: Tinashe M.
    family-names: Tapera
    orcid: 'https://orcid.org/0000-0001-9080-5010'
  - given-names: M. Dylan
    family-names: Tisdall
    orcid: 'https://orcid.org/0000-0002-0454-3112'
  - given-names: Tien
    family-names: Tong
    orcid: 'https://orcid.org/0000-0002-1940-3198'
  - given-names: Dale
    family-names: Zhou
    orcid: 'https://orcid.org/0000-0001-9240-1327'
  - given-names: Oscar
    family-names: Esteban
    orcid: 'https://orcid.org/0000-0001-8435-6191'
  - given-names: Russell A.
    family-names: Poldrack
    orcid: 'https://orcid.org/0000-0001-6755-0259'
  - given-names: John A.
    family-names: Detre
    orcid: 'https://orcid.org/0000-0002-8115-6343'
  - given-names: Theodore
    family-names: Satterthwaite
    affiliation: University of Pennsylvania
    orcid: 'https://orcid.org/0000-0001-7072-9399'
identifiers:
  - type: doi
    value: 10.5281/zenodo.3759082
    description: The Zenodo DOI
repository-code: 'https://github.com/PennLINC/aslprep'
url: 'https://aslprep.readthedocs.io'
abstract: >-
  Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary
  method for noninvasively measuring regional brain perfusion in humans.
  We introduce ASLPrep, a suite of software pipelines that ensure the
  reproducible and generalizable processing of ASL MRI data.
keywords:
  - ASL
  - BIDS-App
  - Neuroimaging
license: BSD-3-Clause
version: 25.0.0
date-released: '2025-08-28'

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