Characterization of Diffusion Metric Map Similarity in Data From a Clinical Data Repository Using Histogram Distances.

ABSTRACT: As the sharing of data is mandated by funding agencies and journals, reuse of data has become more prevalent. It becomes imperative, therefore, to develop methods to characterize the similarity of data. While users can group data based on the acquisition parameters stored in the file headers, these gives no indication whether a file can be combined with other data without increasing the variance in the data set. Methods have been implemented that characterize the signal-to-noise ratio or identify signal drop-outs in the raw image files, but potential users of data often have access to calculated metric maps and these are more difficult to characterize and compare. Here we describe a histogram-distance-based method applied to diffusion metric maps of fractional anisotropy and mean diffusivi

SUBMITTER: Warner GC 

PROVIDER: S-EPMC5852401 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature