As data reuse becomes more popular, it is critical to develop methods that characterize the similarity of data. Methods have been developed that characterize raw image files, but users often only have access to calculated parameter maps. Here we describe a histogram-distance-based method applied to diffusion metric maps generated from MRI data extracted from a clinical data repository. We find that metric maps from GE scanners are less similar than that from Siemens scanners. We also find within vendor differences at any selection of the acquisition parameters considered here (field strength, number of gradient directions, b-value and vendor).
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