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In this paper, we propose a novel indexing structure, called the target tree, which is designed to efficiently answer a new type of spatial query, called a radial query. A radial query seeks to find all objects in the spatial data set that intersect with line segments emanating from a single, designated target point. Many existing and emerging biomedical applications use radial queries, including surgical planning in neurosurgery. Traditional spatial indexing structures such as the R*-tree and quadtree perform poorly on such radial queries. A target tree uses a regular hierarchical decomposition of space using wedge shapes that emanate from the target point, resulting in an index structure that is very efficient for evaluating radial queries. We present a detailed performance evaluation of the target tree, comparing with the R*-tree and quadtree indexing methods, and show that the target tree method outperforms these existing methods by at least a factor of 2-10.