Analysis of scan-rescan subject identifiability of T1 anatomical brain MRI could provide insight into intersession differences. Here we examine a principal component analysis-based method of maximizing differential identifiability and its effect on T1 brain images with and without added noise. We demonstrate that differential identifiability can be maximized via dataset reconstruction with reduced principle components. This reconstruction results in increased similarity between repeated scans for a given subject as well as apparent reduced intersession noise in images. We conclude that further analysis of maximized differential identifiability could provide insight for future applications in reducing intersession MRI noise.
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