Improved quality of re-sliced MR images using re-normalized sinc interpolation

This article shows that re-normalizing the interpolation kernel for a constant integral can make a significant improvement in performance of sinc interpolation methods. A comparison was performed between standard and re-normalized sinc kernels of various sizes using data from four commonly used magnetic resonance (MR) imaging sequences. Standard rotations were performed and compared with a "gold standard" data set generated by use of a large (13 x 13 x 13) sinc kernel. Measurements of systematic pixel intensity offset error and variance of generated residuals were used to estimate resultant interpolation error. Theoretical estimates of the consequent savings in computation time were compared with the measured time required for each algorithm and with the automated image registration (AIR) program. The use of a small (5 x 5 x 5) re-normalized kernel produced relative errors comparable to those in the gold standard data set, allowing saving in computation time of up to 30 times in comparison with standard sinc interpolation. This approach brings the implementation of MR volume re-slicing much closer to the demands of a clinical environment.