NeuroNorm: An R package to standardize multiple structural MRI
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Title
NeuroNorm: An R package to standardize multiple structural MRIDate
2023Publisher
ElsevierBibliographic citation
PAYARES-GARCIA, David; MATEU, Jorge; SCHICK, Wiebke. NeuroNorm: An R Package to Standardize Multiple Structural MRI. Neurocomputing, 2023, p. 126493.Type
info:eu-repo/semantics/articlePublisher version
https://www.sciencedirect.com/science/article/pii/S0925231223006161Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
Preprocessing of structural MRI involves multiple steps to clean and standardize data before further analysis. Typically, researchers use numerous tools to create tailored preprocessing workflows that adjust to
their ... [+]
Preprocessing of structural MRI involves multiple steps to clean and standardize data before further analysis. Typically, researchers use numerous tools to create tailored preprocessing workflows that adjust to
their dataset. This process hinders research reproducibility and transparency. In this paper, we introduce
NeuroNorm, a robust and reproducible preprocessing pipeline that addresses the challenges of preparing
structural MRI data. NeuroNorm adapts its workflow to the input datasets without manual intervention
and uses state-of-the-art methods to guarantee high-standard results. We demonstrate NeuroNorm’s
strength by preprocessing hundreds of MRI scans from three different sources with specific parameters
on image dimensions, voxel intensity ranges, patients characteristics, acquisition protocols and scanner
type. The preprocessed images can be visually and analytically compared to each other as they share
the same geometrical and intensity space. NeuroNorm supports clinicians and researchers with a robust,
adaptive and comprehensible preprocessing pipeline, increasing and certifying the sensitivity and validity of subsequent analyses. NeuroNorm requires minimal user inputs and interaction, making it a userfriendly set of tools for users with basic programming experience. [-]
Is part of
Neurocomputing, 2023Funder Name
National Institutes of Health (NIH) - USA | United States Department of Defense
Project code
U01 AG024904 | W81XWH-12-2-0012
Rights
info:eu-repo/semantics/openAccess
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