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ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion

Nature Methods volume 19, pages 683–686 (2022)Cite this article

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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.

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Fig. 1: Overview of ASLPrep.
Fig. 2: ASLPrep quantifies CBF across sequences, scanners and the lifespan.

Data availability

All quantified data are available as Source Data files provided with this paper. Additionally, raw data are available for many of the datasets used in evaluation of the software, depending on the original source of the data. PNC data are available on dbGAP (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000607.v3.p2). NKI neuroimaging data are openly available on the NeuroImaging Tools and Resources Collaboratory (https://fcon_1000.projects.nitrc.org/indi/enhanced/). AGE data are available on Open Neuro (https://openneuro.org/datasets/ds000240/versions/00002). For the remaining datasets, we do not control the distribution of them, but requested can be made to the original authors. The IRR dataset will be released publicly; at present, it is available on request from the corresponding author. Access to the FTD dataset is governed by the ALLFTD Consortium.

Code availability

ASLPrep is available under the BSD-3-clause license at https://github.com/pennlinc/aslprep. Docker images corresponding to every new release of ASLPrep are automatically generated and made available on Docker Hub (https://hub.docker.com/r/pennlinc/aslprep). All code used to perform the statistical tests are available at: https://pennlinc.github.io/aslprep_paper, under the BSD-3-Clause License. Software documentation is available at https://aslprep.readthedocs.io. ASLPrep code is also available through Zenodo: https://doi.org/10.5281/zenodo.4815777 (ref. 55) and as part of a Code Ocean compute capsule: https://doi.org/10.24433/CO.7220174.v1 (ref. 56).

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Acknowledgements

We acknowledge the support of the following grants: nos. RF1MH121867 (O.E., R.A.P., T.D.S., M.M.), R01MH120482 (T.D.S. and M.M.), R01EB022573 (C.D. and T.D.S.), F31MH126569 (D.Z.), R01MH123550 (T.D.S.), T32MH019112 (E.B.B. and R.E.G.), R01MH120811 (D.J.O.), R01MH112847 (T.D.S.), R01MH112070 (C.D.), RF1AG054409 (C.D.), U01AG052943 (C.M.), U19AG063911 (B.B. and H.R.), AG062422 (H.R.), AG045333 (H.R.), AG019724 (H.R.), P01AG066597 (C.M.), P41EB015893 (J.A.D.), R01MH111886 (D.J.O.), R01MH119219 (R.E.G. and R.C.G.), R01AG054519 (J.S.P.), K01AG061277 (J.S.P.), R01MH120174 (D.R.R.), R01MH119185 (D.R.R.), R03AG063213 (S.D.), R01NS111115 and P30AG072979 (J.A.D.), and the Swiss National Science Foundation - Ambizione 185872 (O.E.). Support was provided by the Dutch Heart Foundation (2020T049; H.J.M.M.M.), by the Eurostars-2 joint program (H.J.M.M.M.) with cofunding from the European Union Horizon 2020 research and innovation program (ASPIRE E!113701) provided by the Netherlands Enterprise Agency (RvO), the EU Joint Program for Neurodegenerative Disease Research provided by the Netherlands Organization for health Research and Development and Alzheimer Nederland (DEBBIE JPND2020-568-106; H.J.M.M.M.), and the AE foundation (T.D.S.). Additional support was provided by the Center for Biomedical Image Computing and Analytics (CBICA; A.A., M.C., T.D.S. and C.D.) the Penn-CHOP Lifespan Brain Institute (LiBI; T.D.S., R.C.G. and R.E.G.).

Author information

Authors and Affiliations

  1. Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Azeez Adebimpe, Maxwell Bertolero, Matthew Cieslak, Kristin Murtha, Erica B. Baller, Sydney Covitz, Diego G. Davila, Basma Jaber, Tinashe M. Tapera, Dale Zhou & Theodore D. Satterthwaite

  2. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Azeez Adebimpe, Maxwell Bertolero, Matthew Cieslak, Kristin Murtha, Erica B. Baller, Ellyn R. Butler, Sydney Covitz, Diego G. Davila, Matthew W. Flounders, Raquel E. Gur, Ruben C. Gur, Basma Jaber, Ging-Yuek Robin Hsiung, Desmond J. Oathes, David R. Roalf, Tinashe M. Tapera & Theodore D. Satterthwaite

  3. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Sudipto Dolui, Mark A. Elliott, Will Tackett, M. Dylan Tisdall & John A. Detre

  4. Lifespan Brain Institute, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Erica B. Baller, Raquel E. Gur, Ruben C. Gur, David R. Roalf & Theodore D. Satterthwaite

  5. Department of Neurology, Mayo Clinic, Rochester, MN, USA

    Bradley Boeve, Hugo Botha, Danielle Brushaber, Julie A. Fields, Leah K. Forsberg, Ralitza Gavrilova, Jonathon Graff-Radford, David T. Jones, Kejal Kantarci, David Knopman, Walter Kremers, Maria Lapid, Carly Mester, Vijay Ramanan, Meghana Rao, Rodolfo Savica & Jeremy Syrjanen

  6. Department of Neurology, University of California, San Francisco, CA, USA

    Adam Boxer, Adam L. Boxer, Matthew Hall, Eric Huang, Hilary W. Heuer, John Kornak, Joel Kramer, Argentina Lario Lago, Peter Ljubenkov, Bruce L. Miller, Katherine P. Rankin, Julio Rojas-Martinez, Howard J. Rosen, William Seeley, Adam M. Staffaroni, Jack Taylor, Lawren VandeVrede & Howard Rosen

  7. Penn Image Computing and Science Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Phil Cook

  8. Child Mind Institute, New York, NY, USA

    Stan Colcombe, Alexandre R. Franco & Michael Milham

  9. Center for Brain Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA

    Stan Colcombe, Alexandre R. Franco & Michael Milham

  10. Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Christos Davatzikos

  11. Center for Neuromodulation in Depression and Stress, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Matthew W. Flounders & Desmond J. Oathes

  12. Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Corey McMillian, Christopher A. Olm & Jeffrey S. Phillips

  13. Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands

    Henk J. M. M. Mutsaerts

  14. Penn Brain Science, Translation, Innovation, and Modulation Center, Perelmann School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Desmond J. Oathes

  15. Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

    Oscar Esteban

  16. Department of Psychology, Stanford University, Stanford, CA, USA

    Oscar Esteban & Russell A. Poldrack

  17. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Murray Grossman, David Irwin, Katya Rascovsky & John A. Detre

  18. Indiana University, Indianapolis, IN, USA

    Liana Apostolova, David Clark & Tatiana Foroud

  19. Case Western Reserve University, Cleveland, OH, USA

    Brian Appleby

  20. University of Michigan, Ann Arbor, MI, USA

    Sami Barmada

  21. University of California, Los Angeles, Los Angeles, CA, USA

    Yvette Bordelon, Giovanni Coppola, Daniel Geschwind, Mario Mendez & E. Marisa Ramos

  22. University of North Carolina, Chapel Hill, NC, USA

    Andrea Bozoki & Daniel Kaufer

  23. Vanderbilt University, Nashville, TN, USA

    Ryan Darby

  24. Mayo Clinic, Jacksonville, FL, USA

    Dennis Dickson, Tania Gendron, Neill Graff-Radford, Leonard Petrucelli & Zbigniew Wszolek

  25. University of Washington, Seattle, WA, USA

    Kimiko Domoto-Reilly

  26. National Centralized Repository for Alzheimer’s Disease, Indianapolis, IN, USA

    Kelley Faber & Tatiana Foroud

  27. Washington University, St. Louis, St. Louis, MO, USA

    Anne Fagan

  28. Columbia University, New York, NY, USA

    Jill Goldman & Edward D. Huey

  29. University of California, San Diego, La Jolla, CA, USA

    Douglas R. Galasko, Gabriel Leger & Irene Litvan

  30. University of British Columbia, Vancouver, British Columbia, Canada

    Ian M. Grant & Ian R. Mackenzie

  31. UT Southwestern, Dallas, TX, USA

    Diana Kerwin

  32. Massachusettes General Hospital, Boston, MA, USA

    Diane Lucente, Scott McGinnis & Bonnie Wong

  33. Houston Methodist Hospital, Houston, TX, USA

    Joseph C. Masdeu & M. Belen Pascual

  34. Johns Hopkins University, Baltimore, MD, USA

    Chiadi Onyike

  35. University of Colorado, Boulder, CO, USA

    Peter Pressman

  36. University of Antwerp, Antwerp, Belgium

    Rosa Rademakers

  37. Cleveland Clinic Las Vegas, Las Vegas, NV, USA

    Aaron Ritter

  38. University of Alabama Birmingham, Birmingham, AL, USA

    Erik D. Roberson

  39. University of Toronto, Toronto, Ontario, Canada

    M. Carmela Tartaglia

  40. Northwestern University, Evanston, IL, USA

    Sandra Weintraub

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  1. Azeez Adebimpe
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  2. Maxwell Bertolero
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  3. Sudipto Dolui
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  4. Matthew Cieslak
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  5. Kristin Murtha
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  7. Bradley Boeve
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  8. Adam Boxer
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  9. Ellyn R. Butler
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  10. Phil Cook
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  11. Stan Colcombe
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  12. Sydney Covitz
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  13. Christos Davatzikos
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  14. Diego G. Davila
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  15. Mark A. Elliott
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  16. Matthew W. Flounders
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  17. Alexandre R. Franco
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  19. Ruben C. Gur
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  20. Basma Jaber
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  21. Corey McMillian
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  22. Michael Milham
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  23. Henk J. M. M. Mutsaerts
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  25. Christopher A. Olm
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  26. Jeffrey S. Phillips
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  27. Will Tackett
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  28. David R. Roalf
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  29. Howard Rosen
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  30. Tinashe M. Tapera
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  31. M. Dylan Tisdall
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  32. Dale Zhou
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  33. Oscar Esteban
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  34. Russell A. Poldrack
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  35. John A. Detre
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  36. Theodore D. Satterthwaite
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Consortia

the ALLFTD Consortium

  • Liana Apostolova
  • , Brian Appleby
  • , Sami Barmada
  • , Bradley Boeve
  • , Yvette Bordelon
  • , Hugo Botha
  • , Adam L. Boxer
  • , Andrea Bozoki
  • , Danielle Brushaber
  • , David Clark
  • , Giovanni Coppola
  • , Ryan Darby
  • , Dennis Dickson
  • , Kimiko Domoto-Reilly
  • , Kelley Faber
  • , Anne Fagan
  • , Julie A. Fields
  • , Tatiana Foroud
  • , Leah K. Forsberg
  • , Daniel Geschwind
  • , Jill Goldman
  • , Douglas R. Galasko
  • , Ralitza Gavrilova
  • , Tania Gendron
  • , Jonathon Graff-Radford
  • , Neill Graff-Radford
  • , Ian M. Grant
  • , Murray Grossman
  • , Matthew Hall
  • , Eric Huang
  • , Hilary W. Heuer
  • , Ging-Yuek Robin Hsiung
  • , Edward D. Huey
  • , David Irwin
  • , David T. Jones
  • , Kejal Kantarci
  • , Daniel Kaufer
  • , Diana Kerwin
  • , David Knopman
  • , John Kornak
  • , Joel Kramer
  • , Walter Kremers
  • , Maria Lapid
  • , Argentina Lario Lago
  • , Gabriel Leger
  • , Peter Ljubenkov
  • , Irene Litvan
  • , Diane Lucente
  • , Ian R. Mackenzie
  • , Joseph C. Masdeu
  • , Scott McGinnis
  • , Mario Mendez
  • , Carly Mester
  • , Bruce L. Miller
  • , Chiadi Onyike
  • , M. Belen Pascual
  • , Leonard Petrucelli
  • , Peter Pressman
  • , Rosa Rademakers
  • , Vijay Ramanan
  • , E. Marisa Ramos
  • , Meghana Rao
  • , Katya Rascovsky
  • , Katherine P. Rankin
  • , Aaron Ritter
  • , Erik D. Roberson
  • , Julio Rojas-Martinez
  • , Howard J. Rosen
  • , Rodolfo Savica
  • , William Seeley
  • , Jeremy Syrjanen
  • , Adam M. Staffaroni
  • , M. Carmela Tartaglia
  • , Jack Taylor
  • , Lawren VandeVrede
  • , Sandra Weintraub
  • , Bonnie Wong
  •  & Zbigniew Wszolek

Contributions

A.A., M.B., S.D., M.C., K.M., E.R.B., P.C., S. Covitz., D.G.D., M.A.E., M.W.F., B.J., C.A.O., W.T., T.M.T., O.E., R.A.P., J.A.D. and T.D.S. developed ASLPrep including code, testing and documentation. A.A., M.B., M.C., K.M., D.G.D., M.A.E., S. Colcombe., C.D., M.A., B.B., A.B., A.R.F., R.E.G., R.C.G., C. McMillan., the ALLFTD Consortium, L.A., B.A., S.B., B.B., Y.B., H.B., A.L.B., A.B., D.B., D.C., G.C., R.D., D.D., K.D., K.F., A.F., J.F., T.F., L.K.F., D.G., J.G., D.R.G., R.G., T.G., J.G., N.G., I.G., M.G., M.H., E.H., H.H., G.H., E.H., D.I., D.T.J., K.K., D. Kaufer, D. Kerwin, D. Knopman, J. Kornak, J. Kramer, W.K., M.L., A.L.L., G.L., P.L., I.L., D.L., I.M., J.C.M., S.M., M. Mendez,. C. Mester, B.L.M., C.O., M.B.P., L.P., P.P., R.P., V.R., E.M.R., M.R., K.R., K.P.R., A.R., E.D.R., J.R., H.J.R., R.S., W.S., J.S., A.M.S., M.C.T., J.T., L.V., S.W., B.W., Z.W., M. Milham., D.J.O., J.S.P., D.R.R., H.R., M.D.T., D.Z. and T.D.S. contributed and curated data. A.A., M.B., S.D., M.C., D.Z. and T.D.S. processed and analyzed the data. A.A., M.B., S.D., M.C., K.M.,E.B.B., B.B., A.B., E.R.B., P.C., S. Colcombe, S. Covitz., C.D., M.A.E., D.G.D., M.A.E., M.W.F., A.R.F., R.E.G., R.C.G., B.J., C. McMillan, L.A., B.A., S.B., Y.B., H.B., A.L.B., A.B., D.B., D.C., G.C., R.D., D.D., K.D., K.F., A.F., J.F., T.F., L.K.F., D.G., J.G., D.R.G., R.G., T.G., J.G., N.G., I.G., M.G., M.H., E.H., H.H., G.H., E.H., D.I., D.T.J., K.K., D. Kaufer, D. Kerwin, D. Knopman, J. Kornak, J. Kramer, W.K., M.L., A.L.L., G.L., P.L., I.L., D.L., I.M., J.C.M., S.M., M.Mendez,. C.Mester, B.L.M., C.O., M.B.P., L.P., P.P., R.P., V.R., E.M.R., M.R., K.R., K.P.R., A.R., E.D.R., J.R., H.J.R., R.S., W.S., J.S., A.M.S., M.C.T., J.T., L.V., S.W., B.W., Z.W., M. Milham, H.J.M.M.M., D.J.O., C.A.O., J.S.P., W.T., D.R.R., H.R., T.M.T., M.D.T., D.Z., O.E., R.A.P., J.A.D. and T.D.S. assisted with writing and editing the manuscript.

Corresponding author

Correspondence to Theodore D. Satterthwaite.

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The authors declare no competing interests.

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Peer review information

Nature Methods thanks Luis Hernandez-Garcia, Flora Kennedy McConnell, and Franco Pestilli for their contribution to the peer review of this work. Nina Vogt was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Exemplar data for each dataset and CBF quantification method.

A single participant from each dataset is shown (in MNI space), with CBF quantified using each of four methods. SCRUB could not be applied for the FTD dataset as an ASL timeseries is required; the sequence used for that study provided only a ∆M image.

Source data

Extended Data Fig. 2 Mean cerebral blood flow maps for each dataset and quantification method.

CBF quantification for each dataset using the four methods supported by ASLPrep. An axial slice (z = 0) of the mean CBF image for each dataset is displayed (in MNI space) for each quantification method. SCRUB could not be applied for the FTD dataset as an ASL timeseries is required; the sequence used for that study provided only a single ∆M image.

Source data

Extended Data Fig. 3 Image smoothness from a legacy pipeline and ASLPrep.

a) Comparison of CBF quantified with ASLPrep and a previously published pipeline used for the PNC; both pipelines implemented the standard kinetic model. Image smoothness in template space differed between ASLPrep and the previous pipeline (two-sided t-test; t(1,480) = 252.58, p < 1 × 10−16). b) Across-dataset average image.

Source data

Extended Data Fig. 4 CBF quantified using ASLPrep aligns with a commonly used PET atlas.

a) CBF quantified using ASLPrep (averaged across datasets) aligned with CBF from a commonly used PET atlas included in SPM, where CBF was measured using [15O]water PET (Pearson r = 0.60). b) Assessment of the similarity of the images using a null distribution (comparison with a Brainsmash null p = 0.0001; panel b).

Source data

Extended Data Fig. 5 Bayesian methods mitigate impact of in-scanner motion on CBF image quality.

Impact of motion on the CBF image quality as assessed by the Quality Evaluation Index. The impact of motion on quality differed significantly among quantification approaches (linear mixed effects model, F = 228.09, p = 1.0 × 10−25). The envelope indicates the 95% confidence interval.

Source data

Extended Data Fig. 6 CBF of gray and white matter across datasets.

The distribution of cerebral blood flow (CBF) within grey matter (GM) and white matter (WM) is displayed for each dataset, for each quantification option: the standard CBF model, BASIL (a), BASIL with partial volume correction (PVC; b), and SCRUB (c). SCRUB could not be applied for the FTD dataset as an ASL timeseries is required; the sequence used for that study provided only a single ∆M image. Boxes within each violin plot indicate interquartile range with the median shown as a white dot.

Source data

Extended Data Fig. 7 CBF declines nonlinearly with age over the lifespan.

Evolution of gray matter CBF with age over the lifespan across all five datasets. For each dataset, we used four methods for quantifying CBF: the standard CBF model (see main text), BASIL (a), BASIL with partial volume correction (PVC; b), and SCRUB (c). We used a generalized additive model with penalized splines to characterize the nonlinear evolution of CBF over age. The thick black line represents the predicted values, while the dashed lines represent the 95% confidence intervals.

Source data

Extended Data Fig. 8 Compute time for ASLPrep.

Distribution of compute time for each dataset, separated by ASL processing and anatomic processing (which relies upon sMRIPrep). Anatomic preprocessing always required a longer duration, with ASL preprocessing and CBF computation requiring less than 70 minutes in all datasets.

Source data

Supplementary information

Source data

Source Data Fig. 2

Summary for CBF values, age and other quality control values for each dataset used.

Source Data Extended Data Fig. 1

Exemplar CBF maps of participants from each dataset.

Source Data Extended Data Fig. 2

Mean CBF maps for each dataset.

Source Data Extended Data Fig. 3

Full-width at half-maximum data of CBF maps generated from data processed by ASLPrep and Previous pipelines.

Source Data Extended Data Fig. 4

CBF maps of ASLPrep and PET.

Source Data Extended Data Fig. 5

Summary for CBF values, age and other quality control values for each dataset used.

Source Data Extended Data Fig. 6

Summary for CBF values, age and other quality control values for each dataset used.

Source Data Extended Data Fig. 7

Summary for CBF values, age and other quality control values for each dataset used.

Source Data Extended Data Fig. 8

Computing time for anatomical and ASL processing.

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Adebimpe, A., Bertolero, M., Dolui, S. et al. ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion. Nat Methods 19, 683–686 (2022). https://doi.org/10.1038/s41592-022-01458-7

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