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 user-friendly set of tools for users with basic programming experience.
Keywords
MRI processing
Standardization
Neurodegenerative disorders
R
Data availability
The authors do not have permission to share data.
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David Payares-Garcia is a cadastral engineer and geodesist with an Erasmus Mundus Master degree in Geospatial Technologies from the University of Munster, Universität Jaume I and Universidade NOVA de Lisboa. He is currently a PhD candidate in the Earth Observation Department at the ITC Faculty Geo-Information Science and Earth Observation (University of Twente. His research interests focus mainly on applying spatial data science and geoinformatics for solving medical and epidemiological problems. The fields of application David works in include spatial epidemiology, spatio-temporal modeling, biostatistics, and medical geography. He also have experience in other areas such as software development, remote sensing, and demography.
Jorge Mateu graduated in mathematical sciences from the University of Valencia, Spain, where he also received the Ph.D. degree, with long visiting periods to the University of Lancaster, U.K., with Prof. Peter Diggle. He is currently a Full Professor of Statistics with the Department of Mathematics, Jaume I University, Castellón, where he has worked for the past 20 years. He is also the Director of the Unit “Statistical Modelling of Crime Data”, based in the Department of Mathematics, Jaume I University, Castellón, and the Co-Director of the Erasmus Mundus Master in Geospatial Technologies, funded by the European Commission. He has published more than 250 articles in peer-reviewed international journals, and he is coauthor of several proceedings and research books. His main fields of interest include stochastic processes in their wide sense with a particular focus on spatial and spatio-temporal point processes and geostatistics. He has organized several international conferences with a focus on modeling space-time processes, and leads the organizing committee of a series of biannual conferences (called METMA, ten by now) co-sponsored by TIES, for which he was also a Secretary.
Wiebke Schick is a researcher and lecturer in spatial cognition. Her background is interdisciplinary and diverse: she received a Magister Artium with the subjects general rhetorics, newer English literature, and psychology, and was then a PhD student at a Graduate Training Center of Neuroscience. Now She is a postdoc in geoinformatics at the University of Munster. She uses approaches and methods from the fields of linguistics, cognitive sciences and psychology to investigate the communication of spatial information as well as their acquisition and storage. Also, She is interested in communicating science to a broad audience: She is available for scientific talks and regularly participate at science slams since 2015.
