Automatic registration for multiple sclerosis change detection

The authors are developing an automated 3D change detection system which accurately registers medical imagery (e.g., MRI or CT) of the same patient from different times for diagnosing pathologies, monitoring treatment, and tracking tissue changes. The system employs a combination of energy-minimization registration techniques to achieve accurate and robust alignment of 3D data sets. The bases for the registration are 3D surfaces extracted from the 3D imagery. Resultant structural changes in the data are identified by using an adaptive segmentation technique to automatically determine tissue morphology. The novel contributions of this work are its end-to-end automation of the change detection process and its high accuracy in monitoring and highlighting such physiological changes. The authors have applied this system to a multiple sclerosis study in which each patient had been imaged over 20 times for the purpose of tracking lesion evolution. This report describes preliminary registration performance analysis using this data