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Emergence of Groupwise Registration in MR Brain Study
Abstract and Figures
1. Background Modern medical imaging technologies such as Magnetic Resonance Imaging (MRI)  offer a safe and non-invasive means of performing clinical diagnosis and research, involving human brain development, aging, and disease-induced anomalies. For reliable estimation of disease related micro-structural difference, accurate deformable image registration plays a key fundamental role in dealing with confounding intra-subject variability in longitudinal studies and inter-subject variability in cross-sectional studies. Pairwise Registration: A plethora of pairwise deformable registration algorithms have flourished in the past two decades, comprehensive surveys of which can be found in [2-6]. In general, the goal of image registration is to estimate the deformation field for warping the subject image (or moving image) to the template image (or fixed image) by maximizing a certain similarity measure between the warped subject and the template. Upon successful registration, intra-or inter-subject differences are minimized, while at the same time disease-related changes and morphological variations are preserved. The majority of image registration algorithms fall into three categories: landmark-based [7-9], intensity-based [10-15], and feature-based [16-19]. Landmark-based algorithms take advantage of anatomical prior knowledge and are thus computationally fast, since only a few landmarks out of all voxels in an image volume need to be matched. It is however a challenging task even for the trained experts to accurately place a sufficient number of anatomical landmarks for achieving accurate registration. Moreover, inter-rater landmark-placement variability could be large, thus seriously undermining the performance of landmark-based registration algorithms. Intensity-based algorithms * Guorong Wu, Hongjun Jia, and Qian Wang contributed equally to this book chapter.
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