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

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.).

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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.

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Correspondence to Theodore D. Satterthwaite.

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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

Supplementary Information

Supplementary Fig. 1 and Tables 1–4.

Reporting Summary

Peer Review File

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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