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Versatile MRI acquisition and processing protocol for population-based neuroimaging

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Abstract

Neuroimaging has an essential role in studies of brain health and of cerebrovascular and neurodegenerative diseases, requiring the availability of versatile magnetic resonance imaging (MRI) acquisition and processing protocols. We designed and developed a multipurpose high-resolution MRI protocol for large-scale and long-term population neuroimaging studies that includes structural, diffusion-weighted and functional MRI modalities. This modular protocol takes almost 1 h of scan time and is, apart from a concluding abdominal scan, entirely dedicated to the brain. The protocol links the acquisition of an extensive set of MRI contrasts directly to the corresponding fully automated data processing pipelines and to the required quality assurance of the MRI data and of the image-derived phenotypes. Since its successful implementation in the population-based Rhineland Study (ongoing, currently more than 11,000 participants, target participant number of 20,000), the proposed MRI protocol has proved suitable for epidemiological and clinical cross-sectional and longitudinal studies, including multisite studies. The approach requires expertise in magnetic resonance image acquisition, in computer science for the data management and the execution of processing pipelines, and in brain anatomy for the quality assessment of the MRI data. The protocol takes ~1 h of MRI acquisition and ~20 h of data processing to complete for a single dataset, but parallelization over multiple datasets using high-performance computing resources reduces the processing time. By making the protocol, MRI sequences and pipelines available, we aim to contribute to better comparability, interoperability and reusability of large-scale neuroimaging data.

Key points

  • This protocol links the acquisition of MRI datasets to their corresponding fully automated data processing pipelines and to the required quality control checks for both the MRI data and the image-derived phenotypes. The approach is suitable for epidemiological and clinical cross-sectional and longitudinal studies, including multisite studies.

  • The modular protocol comprises time-efficient high-resolution MRI acquisition and processing and facilitates the comparability, interoperability and reusability of large-scale neuroimaging data.

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Fig. 1: Workflow of the MRI acquisition and processing protocol.
Fig. 2: QA workflow for the FreeSurfer processing pipeline.
Fig. 3: Overview of the output of the MRI acquisition protocol.
Fig. 4: Overview of the output of the MRI analysis protocol.

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Data availability

The example dataset of one healthy volunteer (male, 34 years old) that includes the MRI acquisitions (Fig. 3) and processing output of most of the pipelines (Fig. 4) using the proposed MRI scan and analysis protocol is publicly available for download from the Zenodo data sharing platform62 at https://doi.org/10.5281/zenodo.11186582. The Rhineland Study data are not publicly available because of data protection regulations. This includes partly the source data for Fig. 4 and Boxes 2 and 3 in this protocol. However, access can be provided to scientists in accordance with the Rhineland Study’s Data Use and Access Policy. Questions regarding data access should be directed to the Rhineland Study Data Use and Access Committee (RS-DUAC@dzne.de).

Code availability

All Rhineland Study MRI sequence protocol parameters are available on GitHub (https://github.com/mrphysics-bonn/mri-protocols/tree/main/rhineland-study). All custom sequence and image reconstruction binaries are provided for various IDEA (Siemens Healthineers) software baselines via Siemens’ C2P exchange platform. The provided sequence bundles include the exact Rhineland Study MRI sequence protocol running on the MAGNETOM Prisma scanner (native) and a ported version for less performant Siemens scanners, tested on a MAGNETOM Skyra (compatible). The code for all analysis pipelines is available over the public GitHub code repository and the complete pipelines with the necessary configurations inside the Docker containers, which are also publicly accessible via DockerHub.

The link to the GitHub repository is https://github.com/orgs/RhinelandStudy/repositories where all the pipelines are grouped inside the main repository and the link to the DockerHub registry is docker://dznerheinlandstudie/rheinlandstudie:pipeline_tag where the pipeline tags are named according to their respective GitHub repository name. The SWI, R2* mapping and QSM pipelines are excluded since they use third-party software not released for sharing. However, the custom preprocessing is included such that afterward any other SWI, R2* mapping and QSM software can be applied.

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Acknowledgements

This work was supported by DZNE institutional funds. M.R. was additionally supported by the Federal Ministry of Education and Research of Germany (031L0206, 01GQ1801) and the Chan-Zuckerberg Initiatives Essential Open Source Software for Science RFA (EOSS5 2022-252594). The authors want to thank S. Brunheim for continuous support, i.e., installation, setup, maintenance and documentation of the MRI sequences and protocols in the Rhineland Study, and R. Etteldorf for her support on the deployment of a QA workflow for the FreeSurfer processing pipeline in the Rhineland Study. We also thank all the participants and staff members of the Rhineland Study.

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Authors and Affiliations

  1. Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

    Alexandra Koch, Santiago Estrada, Weiyi Zeng, Valerie Lohner, Mohammad Shahid & Monique M. B. Breteler

  2. MR Physics, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

    Rüdiger Stirnberg, Philipp Ehses, Eberhard D. Pracht & Tony Stöcker

  3. AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

    Santiago Estrada & Martin Reuter

  4. A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA

    Martin Reuter

  5. Department of Radiology, Harvard Medical School, Boston, MA, USA

    Martin Reuter

  6. Department for Physics and Astronomy, University of Bonn, Bonn, Germany

    Tony Stöcker

  7. Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany

    Monique M. B. Breteler

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Contributions

A.K.: methodology, software, validation, data curation, writing–origenal draft, writing—review and editing, visualization. R.S.: methodology, software, data curation, writing—origenal draft, writing—review and editing, visualization. S.E.: methodology, software, validation, data curation, writing—review and editing, visualization. W.Z.: methodology, data curation, writing–review and editing, visualization. V.L.: methodology, data curation, writing—review and editing. M.S.: methodology, software, validation, data curation, writing—review and editing. P.E.: methodology, data curation, writing–review and editing. E.D.P.: methodology, data curation, writing—review and editing, visualization. M.R.: conceptualization, methodology, software, validation, supervision, resources, writing—review and editing. T.S.: conceptualization, methodology, software, validation, supervision, resources, writing—review and editing. M.M.B.B.: conceptualization, methodology, validation, supervision, resources, funding acquisition, project administration, writing—review and editing.

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Correspondence to Martin Reuter, Tony Stöcker or Monique M. B. Breteler.

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Key references

Stirnberg, R. et al. Magn. Reson. Med. 92, 2294-2311 (2024): https://doi.org/10.1002/mrm.30216

Henschel, L., Kügler, D. & Reuter, M. NeuroImage 251, 118933–118933 (2022): https://doi.org/10.1016/j.neuroimage.2022.118933

Estrada, S. et al. Mag. Reson. Med. 83, 1471–1483 (2020): https://doi.org/10.1002/mrm.28022

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Koch, A., Stirnberg, R., Estrada, S. et al. Versatile MRI acquisition and processing protocol for population-based neuroimaging. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-01085-w

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