Spline interpolation in medical imaging: Comparison with other convolution-based approaches

Interpolation is required in a variety of medical image processing applications. Although many interpolation techniques are known from the literature, evaluations of these techniques for the specific task of applying geometrical transformations to medical images are still lacking. In this paper we present such an evaluation. We consider convolution-based interpolation methods and rigid transformations. The evaluation involves a large number of sine-approximating kernels, including piecewise polynomial and windowed sine kernels, and images from a wide variety of medical image modalities. The results show that for all modalities, spline interpolation constitutes the best trade-off between accuracy and computational cost, and therefore is to be preferred over all other methods.