Open AccessA framework for the analysis of phantom data in multicenter diffusion tensor imaging studies
Author(s) -
Walker Lindsay,
Curry Michael,
Nayak Amritha,
Lange Nicholas,
Pierpaoli Carlo
Publication year - 2013
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.22081
Subject(s) - diffusion mri , outlier , imaging phantom , identification (biology) , computer science , multicenter study , variance (accounting) , parametric statistics , white matter , medical physics , artificial intelligence , pattern recognition (psychology) , statistics , mathematics , medicine , nuclear medicine , magnetic resonance imaging , radiology , pathology , biology , botany , accounting , business , randomized controlled trial
Diffusion tensor imaging (DTI) is commonly used for studies of the human brain due to its inherent sensitivity to the microstructural architecture of white matter. To increase sampling diversity, it is often desirable to perform multicenter studies. However, it is likely that the variability of acquired data will be greater in multicenter studies than in single‐center studies due to the added confound of differences between sites. Therefore, careful characterization of the contributions to variance in a multicenter study is extremely important for meaningful pooling of data from multiple sites. We propose a two‐step analysis framework for first identifying outlier datasets, followed by a parametric variance analysis for identification of intersite and intrasite contributions to total variance. This framework is then applied to phantom data from the NIH MRI study of normal brain development (PedsMRI). Our results suggest that initial outlier identification is extremely important for accurate assessment of intersite and intrasite variability, as well as for early identification of problems with data acquisition. We recommend the use of the presented framework at frequent intervals during the data acquisition phase of multicenter DTI studies, which will allow investigators to identify and solve problems as they occur. Hum Brain Mapp 34:2439–2454, 2013. © 2012 Wiley Periodicals, Inc.