Normalized accurate measurement of longitudinal brain change

Purpose: Quantitative 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 a utomation. A fully automated method has been developed that achieves high e stimation accuracy. Method: A fully automated method of longitudinal change analysis is present ed here, which automatically segments bl ain from nonbrain in each image, r egisters the two brain images while using estimated skull images to constra in scaling and skew, and finally estimates brain surface motion by tracking surface points to subvoxel accuracy. Results and Conclusion: The method described has been shown to be accurate (approximate to0.2% brain volume change error) and to achieve high robustne ss (no failures in several hundred analyses over a range of different data sets).