Journal Article (Review Article)

DZNE-2025-00605

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Versatile MRI acquisition and processing protocol for population-based neuroimaging.

Koch, A. (First author)DZNE* ; Stirnberg, R.DZNE* ; Estrada, S.DZNE* ; Zeng, W.DZNE* ; Lohner, V.DZNE* ; Shahid, M.DZNE* ; Ehses, P.DZNE* ; Pracht, E. D.DZNE* ; Reuter, M.DZNE* ; Stöcker, T.DZNE* ; Breteler, M. M. B. (Last author)DZNE*

2025
Nature Publishing Group Basingstoke

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Please use a persistent id in citations: doi:10.1038/s41596-024-01085-w

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.

Keyword(s): Humans (MeSH) ; Magnetic Resonance Imaging: methods (MeSH) ; Neuroimaging: methods (MeSH) ; Image Processing, Computer-Assisted: methods (MeSH) ; Brain: diagnostic imaging (MeSH)

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Contributing Institute(s):

1. Population Health Sciences (AG Breteler)
2. MR Physics (AG Stöcker)
3. Artificial Intelligence in Medicine (AG Reuter)

Research Program(s):

1. 354 - Disease Prevention and Healthy Aging (POF4-354) (POF4-354)

Experiment(s):

1. Rhineland Study / Bonn

Appears in the scientific report 2025

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DEAL Nature ; Essential Science Indicators ; IF >= 10 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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