Abstract #3207

A Novel Method for Determining the Reliability of Diffusion-Weighted Imaging Data

Karl G. Helmer¹, Ming-Chung Chou², Ronny I. Preciado, Allen Song³, Jessica Turner⁴, Barjor Gimi⁵, Susumu Mori⁶,⁷

¹Radiology, Massachusetts General Hospital, Charlestown, MA, United States; ²Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan; ³Radiology, Duke University, Durham, NC, United States; ⁴Translational Neuroscience, The Mind Research Network, Albuquerque, NM, United States; ⁵Radiology and Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States; ⁶Radiology, Johns Hopkins School of Medicine, Baltimore, MD, United States; ⁷Kennedy Kreiger Institute, Baltimore, MD, United States

We report on a method using histogram-similarity measures to establish normative data which can be used in the calibration of diffusion-weighted imaging data collected at a single or multiple sites. This method can be used to quantitatively determine the quality of new data acquired over time by comparing it to the normative data. We have calculated histogram similarity using different metric types, both within and between sites and have shown that these metrics are sensitive measures for both fractional anisotropy (FA) and mean diffusivity (MD). Statistical significance of the results was determined using simulations of two different histogram distributions.