Local Volume Change Maps in Nonrigid Registration: When Are Computed Changes Real? Igor Yanovsky 1 , Paul M. Thompson 2 , Andrea D. Klunder 2 , Arthur W. Toga 2 , and Alex D. Leow 2 1 Department of Mathematics, University of California, Los Angeles, CA 90095, USA 2 Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA 90095, USA {yanovsky@math., thompson@loni., aklunder@loni., toga@loni., feuillet@ }ucla.edu Abstract. Measures of brain change can be computed from sequential MRI scans, providing valuable information on disease progression. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy. In this paper, we examine the reproducibility and stability of different techniques in TBM. In particular, we compare matching functionals (sum of squared differences and mutual information), and registration schemes (unbiased large-deformation registration and viscous fluid registration) using serial MRI scans of nine normal elderly subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results show that the unbiased large-deformation method has higher reproducibility. When coupled with unbiased registration, sum of squared differences outperforms mutual information. In contrast, when coupled with fluid registration, mutual information outperforms sum of squared difference. Moreover, the regions with least stability, due to both spatial distortion and intensity inhomogeneity, are the brain stem, thalamus, and ventricles. Keywords: Mutual information, Image registration, Computational anatomy. 1 Introduction In recent years, computational neuroimaging has become an exciting interdisciplinary field with many applications in functional and anatomic brain mapping, image-guided surgery, and multimodality image fusion [1-3]. The goal of image registration is to align, or spatially normalize, one image to another. In multi-subject studies, this reduces subject-specific anatomic differences by deforming individual images onto a population average brain template. When applied to serial scans of human brain, image registration offers tremendous power in detecting the earliest signs of illness, understanding normal brain development or aging, and monitoring disease progression. Recently, there has been an expanding literature on various nonrigid registration techniques, with different image matching functionals, regularization schemes, and implementation details. In [6], we systematically examined the statistical properties of Jacobian maps (the determinant of local Jacobian operator applied to deformations), and proposed the unbiased large-deformation image