Get access

A framework for the analysis of phantom data in multicenter diffusion tensor imaging studies

Authors

  • Lindsay Walker,

    Corresponding author
    1. Center for Neuroscience and Regenerative Medicine at the Uniformed Services University of the Health Sciences, Bethesda, Maryland
    • Program on Pediatric Imaging and Tissue Sciences, NICHD, NIH, Bethesda, Maryland
    Search for more papers by this author
  • Michael Curry,

    1. Program on Pediatric Imaging and Tissue Sciences, NICHD, NIH, Bethesda, Maryland
    Search for more papers by this author
  • Amritha Nayak,

    1. Program on Pediatric Imaging and Tissue Sciences, NICHD, NIH, Bethesda, Maryland
    2. Center for Neuroscience and Regenerative Medicine at the Uniformed Services University of the Health Sciences, Bethesda, Maryland
    Search for more papers by this author
  • Nicholas Lange,

    1. Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
    2. Department of and Biostatistics, Harvard School of Public Health, Boston, Massachusetts
    Search for more papers by this author
  • Carlo Pierpaoli,

    1. Program on Pediatric Imaging and Tissue Sciences, NICHD, NIH, Bethesda, Maryland
    Search for more papers by this author
  • the Brain Development Cooperative Group


Building 13, Rm 3W16D, 13 South Dr., Bethesda, MD 20892. E-mail: walkerlin@mail.nih.gov

Abstract

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.

Ancillary