Normalized Accurate Measurement of Longitudinal Brain Change


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Abstract

PurposeQuantitative measurement of change in brain size and shape (e.g., to estimate atrophy) is an important current area of research. New methods of change analysis attempt to improve robustness, accuracy, and extent of automation. A fully automated method has been developed that achieves high estimation accuracy.MethodA fully automated method of longitudinal change analysis is presented here, which automatically segments brain from nonbrain in each image, registers the two brain images while using estimated skull images to constrain scaling and skew, and finally estimates brain surface motion by tracking surface points to subvoxel accuracy.Results and ConclusionThe method described has been shown to be accurate (≈0.2% brain volume change error) and to achieve high robustness (no failures in several hundred analyses over a range of different data sets).

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