Shape-based interpolation of gray-scale serial slice images

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Abstract
A new algorithm for interpolation between grayscale serial slice images is presented. The new algorithm (SBIG) applies shape-based interpolation to CT and MRI images and outputs a dense image volume. Unlike algorithms, such as linear interpolation, which rely only on pixel position, SBIG makes essential use of image content to interpolate between pixels and structure of similar shape and intensity which may differ in size and position from slice to slice. A quantitative comparison shows that SBIG significantly outperforms linear interpolation, particularly as the distance between slices increases. More importantly, while linear interpolation demonstrates characteristic low-pass smearing of object edges and detail, high frequency image features such as object edges and anatomical structures are preserved and well approximated with SBIG. As a result, reconstructed coronal slices from the dense image volume using SBIG demonstrate significantly smoother representation of anatomical structures and less 'staircasing' than those created using linear interpolation.

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