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QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data

Nature Methods volume 18, pages 775–778 (2021)Cite this article

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Abstract

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.

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Fig. 1: QSIPrep workflows.
Fig. 2: QSIPrep improves image quality without additional smoothing.

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Data availability

Source data for Fig. 2 and Supplementary Tables 1–4 are available at https://pennlinc.github.io/qsiprep_paper/ (DOI 10.5281/zenodo.4667846). In addition, source data for Fig. 2 and Extended Data Fig. 2 are provided with this paper. Most imaging data are available from public repositories. PNC data (DTI64) are available on dbGAP (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000607.v3.p2). ABCD data are publicly available in both raw BIDS (https://nda.nih.gov/edit_collection.html?id=3165) and preprocessed (https://nda.nih.gov/edit_collection.html?id=2573) states. HBN data are available on the NeuroImaging Tools and Resources Collaboratory (https://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/sharing_neuro.html) under a data use agreement. Other imaging datasets did not obtain explicit participant consent for data to be placed in a public repository. HCP-Lifespan imaging data are available on request to G.K.A. DSI258 is available on request to J.M.V. DSI 756 is available on request to D.S.B. CS-DSI and MultiShell 113 are available on request to T.D.S. Source data are provided with this paper.

Code availability

All code used to perform the statistical tests are available at: https://pennlinc.github.io/qsiprep_paper/ (ref. 44) and the QSIPrep source code is available at github.com/pennbbl/qsiprep. Docker images for the versions of QSIPrep used in this paper are available on DockerHub at v.0.8.0 and 0.9.0beta1. Newer versions are available. A compute capsule containing QSIPrep and example data is available on Code Ocean45.

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Acknowledgements

Funding came from the following: grant nos. R01 MH111886 for D.J.O., D.S.B and T.D.S., NICHD R01HD09586101 to J.D.Y., NINDS R01-NS099348-01 for X.H. and D.S.B., UL1TR001878 for J.D. and M.B.K., 1 U01 EY025864-01 to G.K.A., P30 EY001583 to the Vision Research Center, MH080243 and Staunton Farm Foundation for B.S.L., T32 MH 018951 for L.M.C., R01MH113550, RF1MH116920, R01MH120482 for T.D.S., CBICA Software Seed grants for M.C. and A.A., no. R01-EB027585-01 for E.G., S.F., A.R., A.H.R. and A.K., W911NF-16-1-0474 from the Army Research Office and by the Institute for Collaborative Biotechnologies under Cooperative Agreement no. W911NF-19-2-0026 with the Army Research Office to S.T.G. and J.M.V., RF1AG054409 to C.D. B.S.L. was supported by grant no. T32MH014654. Support for the collection of the data for Philadelphia Neurodevelopment Cohort (PNC) was provided by grant no. RC2MH089983 awarded to R.E.G. Data used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org), held in the NIMH Data Archive. This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9–10 years and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123 and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. This paper reflects the views of the authors and may not reflect the opinions or views of the National Institutes of Health or ABCD consortium investigators.

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Authors and Affiliations

  1. University of Pennsylvania, Philadelphia, PA, USA

    Matthew Cieslak, Philip A. Cook, Xiaosong He, Azeez Adebimpe, Geoffrey K. Aguirre, Danielle S. Bassett, Josiane Bourque, Christos Davatzikos, John A. Detre, Mark A. Elliott, Panagiotis Fotiadis, Ruben C. Gur, Raquel E. Gur, Max B. Kelz, Bart S. Larsen, Allyson P. Mackey, Desmond J. Oathes, Adam R. Pines, David R. Roalf, Valerie J. Sydnor, Tinashe M. Tapera, Ursula A. Tooley & Theodore D. Satterthwaite

  2. University of Pittsburgh, Pittsburgh, PA, USA

    Fang-Cheng Yeh, Laura M. Cabral, Will Foran & Beatriz Luna

  3. Murdoch Children’s Research Institute, Melbourne, Victoria, Australia

    Thijs Dhollander

  4. Indiana University, Bloomington, IN, USA

    Richard F. Betzel, Shreyas Fadnavis & Eleftherios Garyfallidis

  5. Oregon Health and Science University, Portland, OR, USA

    Eric Earl & Anders Perrone

  6. University of Minnesota, Minneapolis, MN, USA

    Damien A. Fair & Anders Perrone

  7. University of California, Santa Barbara, Santa Barbara, CA, USA

    Barry Giesbrecht & Scott T. Grafton

  8. University of Washington, Seattle, WA, USA

    Anisha Keshavan, Adam Richie-Halford & Ariel Rokem

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

    Michael P. Milham

  10. Army Research Laboratoriess, Aberdeen, MD, USA

    Jean M. Vettel

  11. Stanford University, Stanford, CA, USA

    Jason D. Yeatman

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Contributions

M.C., P.A.C., X.H., F.-C.Y., T.D., A.A., M.A.E., S.F., W.F., E.G., A.K., A.R.-H., J.B., L.M.C., P.F., T.M.T., A.P.M., V.J.S., U.A.T., J.D.Y., S.T.G. and T.D.S. developed QSIPrep by code contribution, testing and documentation. Data were collected, curated and shared by G.K.A., D.S.B., R.F.B., C.D., J.A.D., E.E., D.A.F., B.G., R.C.G., R.E.G., M.B.K., B.S.L., A.P.M., M.P.M., D.J.O., A.P., A.R., J.M.V. and S.T.G. Preprocessing pipelines (scheme-specific or QSIPrep) were run and shared by G.K.A., R.F.B., J.B., P.F., A.K., A.R.P., D.R.R., A.R.-H. and A.R. Statistical tests were designed and implemented by M.C., B.S.L., T.M.T. and T.D.S.

Corresponding authors

Correspondence to Matthew Cieslak or Theodore D. Satterthwaite.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Jonathan D. Clayden and the other, anonymous, reviewer(s) 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.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Diffusion imaging data used in QSIPrep development and evaluation.

Cartesian (DSI), random (CS-DSI), and shelled (single-shell DTI and multi-shell) sequences were used to test the preprocessing and reconstruction workflows in QSIPrep. Sequences varied widely in their maximum b-value (1000–5000 s/mm2), number of q-space samples (64–789) and voxel size (1.5–2.3 mm). The row colors represent these schemes across all figures. The colors in the HCP-Lifespan image indicate that these samples came from different scans, grouped by phase-encoding direction.

Extended Data Fig. 2 Comparing added smoothness from QSIPrep and previous pipelines.

Preprocessing generally increases the spatial smoothness of images relative to the raw images. Here the raw image smoothness (x-axis) is compared to the same images after being processed by the published pipeline for each dataset (left) and QSIPrep (right). The direct comparison between QSIPrep and the Previous Pipeline is presented in Fig. 2.

Source data

Extended Data Fig. 3 QSIPrep reconstruction workflows produce comparable output across diverse sampling schemes and reconstruction methods.

Four sampling schemes each reconstructed using four methods: GQI from DSI Studio, multi-tissue CSD from MRtrix, and MAPL from Dipy. ODF fields are shown in two white matter regions (left), a single fiber area in the corpus callosum (top) and a crossing fiber region in the centrum semiovale (bottom). The middle panel shows ODFs reconstructed in the single fiber region, and the right panel shows ODFs reconstructed in the crossing fiber region for the four sampling schemes (rows) and the three reconstruction methods (columns).

Supplementary information

Supplementary Information

Supplementary Figs. 1–3, Tables 1–5 and Notes 1–4.

Reporting Summary

Source data

Source Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

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Cieslak, M., Cook, P.A., He, X. et al. QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data. Nat Methods 18, 775–778 (2021). https://doi.org/10.1038/s41592-021-01185-5

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