NeuroNorm: An R package to standardize multiple structural MRI
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
NeuroNorm: An R package to standardize multiple structural MRIFecha de publicación
2023Editor
ElsevierCita bibliográfica
PAYARES-GARCIA, David; MATEU, Jorge; SCHICK, Wiebke. NeuroNorm: An R Package to Standardize Multiple Structural MRI. Neurocomputing, 2023, p. 126493.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.sciencedirect.com/science/article/pii/S0925231223006161Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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. [-]
Publicado en
Neurocomputing, 2023Entidad financiadora
National Institutes of Health (NIH) - USA | United States Department of Defense
Código del proyecto o subvención
U01 AG024904 | W81XWH-12-2-0012
Derechos de acceso
info:eu-repo/semantics/openAccess
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