Abstract #5015
Assessing the variability of brain diffusion MRI preprocessing pipelines using a Region-of-Interest analysis
Jelle Veraart1, Stefan Winzeck2,3, Álvaro Planchuelo-Gómez4,5, Björn Fricke6, Evgenios N. Kornaropoulos7,8, Harri Merisaari9,10, Tomasz Pieciak5, Yukai Zou11,12, and Maxime Descoteaux13
1Center for Biomedical Imaging, Dept. Radiology, NYU Grossman School of Medicine, New York, NY, United States, 2BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom, 3Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, United Kingdom, 4CUBRIC, Cardiff University, Cardiff, United Kingdom, 5ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain, 6Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 7CRMBM, Aix-Marseille University, Marseille, France, 8Diagnostic Radiology, Lund University, Lund, Sweden, 9Department of Radiology, University of Turku, Turku, Finland, 10TBMC, University of Turku, Turku, Finland, 11Medical Physics Department, University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom, 12Clinical Neurosciences, Clinical and Experimental Sciences, University of Southampton, Southampton, United Kingdom, 13Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Université de Sherbrooke,, Sherbrooke, QC, Canada
Synopsis
Keywords: Diffusion/other diffusion imaging techniques, Data ProcessingThe lack of a standardized preprocessing pipeline is a significant source of variability that might lower the reproducibility of studies, especially across sites and with incomplete description of the preprocessing workflows. We evaluate the downstream impact of variability in preprocessing workflow by quantifying the reproducibility and variability of region-of-interest (ROI) analyses. While many pipelines achieve excellent reproducibility in most ROI, we observed a large variability in performance of preprocessing workflows to the extent that some pipelines are detrimental to the data quality and reproducibility.
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