Reconstruction of 3D medical images: a nonlinear interpolation technique for reconstruction of 3D medical images

摘要

[[abstract]]Three-dimensional medical images reconstructed from a series of two-dimensional images produced by computerized tomography, magnetic resonance imaging, etc., present a valuable tool for modern medicine. Usually, the interresolution between two cross sections is less than the intraresolution within each cross section. Therefore, interpolations are required to create a 3D visualization. Many techniques, including voxel-based and patch tiling methods, apply linear interpolations between two cross sections. Although those techniques using linear interpolations are economical in computation, they need much cross-sectional data and are unable to enlarge because of aliasing. Hence, the techniques that apply two-dimensional nonlinear interpolation functions among cross sections were proposed. The authors introduce the curvature sampling of the contour of a medical object in a CT (computerized tomography) image. Those sampled contour points are the candidates for the control points of Hermite surfaces between each pair of cross sections. Then, a nearest-neighbor mapping of control points between every two cross sections is used for surface formation. The time complexity of the mapping algorithm is O(m+n), where m and n are the numbers of control points of two cross sections. It is much faster than Kehtarnavaz and De Figueiredo's merge method, whose time complexity is O(n3m2) .