Leon Y Cai1, Qi Yang2, Praitayini Kanakaraj2, Vishwesh Nath2, Allen T Newton3,4, Heidi A Edmonson5, Jeffrey Luci6,7, Benjamin N Conrad8,9, Gavin R Price9, Colin B Hansen2, Cailey I Kerley2, Karthik Ramadass2, Fang-Cheng Yeh10, Hakmook Kang11, Eleftherios Garyfallidis12, Maxime Descoteaux13, Francois Rheault2,13, Kurt G Schilling3,4, Bennett A Landman1,2,3,4. 1. Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA. 2. Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA. 3. Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 4. Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA. 5. Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA. 6. Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA. 7. Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA. 8. Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 9. Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA. 10. Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA. 11. Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 12. Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, USA. 13. Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada.
Abstract
PURPOSE: Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge. METHODS: To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. RESULTS: We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. CONCLUSIONS: This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.
PURPOSE: Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge. METHODS: To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. RESULTS: We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. CONCLUSIONS: This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.
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