Spatial Normalization of 3D Brain Images Using Deformable Models

PURPOSE: The spatial normalization and registration of tomographic images from different subjects is a major problem in several medical imaging areas, including functional image analysis, morphometrics, and computer-aided neurosurgery. The focus of this article is the development of a computerized methodology for the spatial normalization of 3D images. METHOD: We propose a technique that is based on geometric deformable models. In particular, we first describe a deformable surface algorithm that finds a mathematical representation of the outer cortical surface. Based on this representation, a procedure for obtaining a map between corresponding regions of the outer cortex in two different images is established. This map is subsequently used to derive a 3D elastic warping transformation, which brings two images into register. RESULTS: The performance of our algorithm is demonstrated on several datasets. In particular, we first test our deformable surface algorithm on MR images. We then register MR images to atlas images. In our third experiment, we apply a procedure for matching distinct cortical features identified through the curvature map of the outer cortex. Finally, we apply our technique to images from elderly individuals with substantial ventricular enlargement, and we show a good registration in the ventricular area and the surrounding brain structures. CONCLUSION: We present a highly automated methodology for spatial normalization of images, using deformable models. Applications of our methodology include stereotactic normalization of functional and structural images, morphological analysis of the brain, and computer-aided neurosurgery.