626 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 25, NO. 5, MAY 2006 Deformation-Based Mapping of Volume Change From Serial Brain MRI in the Presence of Local Tissue Contrast Change Colin Studholme* , Member, IEEE, Corina Drapaca, Bistra Iordanova, and Valerie Cardenas, Member, IEEE Abstract—This paper is motivated by the analysis of serial structural magnetic resonance imaging (MRI) data of the brain to map patterns of local tissue volume loss or gain over time, using registration-based deformation tensor morphometry. Specifically, we address the important confound of local tissue contrast changes which can be induced by neurodegenerative or neurodevelopmental processes. These not only modify apparent tissue volume, but also modify tissue integrity and its resulting MRI contrast parameters. In order to address this confound we derive an approach to the voxel-wise optimization of regional mutual information (RMI) and use this to drive a viscous fluid deformation model between images in a symmetric registration process. A quantitative evaluation of the method when compared to earlier approaches is included using both synthetic data and clinical imaging data. Results show a significant reduction in errors when tissue contrast changes locally between acquisitions. Finally, examples of applying the technique to map different patterns of atrophy rate in different neurodegen- erative conditions is included. Index Terms—Deformation morphometry, magnetic reso- nance imaging, mutual information, tissue contrast, viscous fluid registration. I. INTRODUCTION S ERIAL structural magnetic resonance imaging (MRI) of the brain [1], [2] is an increasingly useful tool in the study of neurodegenerative conditions [3]–[10]. Multiple high resolution structural images of a subject’s brain are acquired over time, al- lowing the progression of tissue volume changes to be tracked in fine detail at all anatomical locations. The imaging technique has been used to detect and characterize disease and has promise in tracking the effects of treatments over time, and thus pro- viding a surrogate marker for cognitive decline, that may be more sensitive than conventional repeated neuropsychological testing. As a result, the analysis of serial structural MRI data has Manuscript received October 12, 2005; revised January 25, 2006. This work was supported by the Whitaker Foundation under Grant RG-01-0115 and in part by the National Institute of Mental Health under Grant MH65392-01. Asterisk indicates corresponding author. *C. Studholme is with the Department of Radiology, University of California San Francisco, Northern California Institute for Research and Education, Vet- erans Affairs Medical Center, 4150 Clement Street, San Francisco, CA 94121 USA (e-mail: colin.studholme@ieee.org). C. Drapaca is with the Magnetic Resonance Research Lab, Mayo Clinic, Rochester, MN 55905-0001 USA (e-mail: Drapaca.Corina@mayo.edu). B. Iordanova is with the Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213 USA (e-mail: biordano@andrew.cmu.edu). V. Cardenas is with the Department of Radiology, University of California San Francisco, Northern California Institute for Research and Education, Vet- erans Affairs Medical Center, San Francisco, CA 94121 USA (e-mail: valerie. cardenas-nicolson@ucsf.edu). Digital Object Identifier 10.1109/TMI.2006.872745 been an active area of image analysis research for many years, with methods ranging from global tissue volume estimation, to finer scale mapping of local change. Traditional computer vision approaches of extracting anatomical boundaries or other simplified object representa- tions from the two scans can be applied. However, because of the complexity of the brain anatomy and the problems posed by limited spatial resolution and contrast, an approach relying on a specific geometric feature, such as a single boundary, is inherently limited in its ability to explore the atrophic process. A more direct approach is to use the first scan as a reference template, containing all subtle structures in the individual, and look for residual geometric differences between that image and later time points. Initial methods taking this approach were based on the analysis of intensity differences between the images, after global rigid alignment of the scans to remove variations in patient positioning [1], [3], [11], [12]. These approaches then estimated global volume changes from the residual intensity differences after global rigid alignment, either through detection of significant intensity difference [12] or through integration over regions of interest [11]. One of the main issues with such approaches is the confound caused by local displacements of tissue over time, which may arise when loss of tissue integrity leads to a collapse of gyral structures. This can induce volume preserving deformations in the neighboring tissue structure, that create significant residual differences between globally rigidly aligned images, leading to artifactual estimates of tissue loss. The most direct form of this in aging is the loss of underlying white matter (WM) leading to the local collapse and displacement of cortical gray matter (GM). The approach to addressing this problem has been to em- ploy nonrigid registration to simultaneously resolve both local tissue size and displacement differences between the two scans [13]–[15]. Nonrigid registration essentially aims to capture all geometric differences between scans in terms of a spatial trans- formation. The components of tissue displacement can then be decomposed from true volume loss, creating a so called defor- mation-based morphometric analysis [16]. A dense field regis- tration-based technique also offers a way of using any subtle contrasts and textures that may be common to both images. Methods for serial MRI analysis published so far have made use of an image similarity term derived directly from intensity differences, to drive the image alignment process. A. Motivation for This Work An important practical confound in serial MRI imaging of both neurodegeneration and development, is the presence of lo- 0278-0062/$20.00 © 2006 IEEE