Comparison of rotation-based methods for iterative reconstruction algorithms

Rotation-based approaches to iterative reconstruction algorithms offer unique trade-offs between accuracy, computational complexity, and smoothing. We analyzed six different rotation methods for generating projections and reconstructions of simulated and clinical data. Nearest neighbor, upsampled nearest neighbor, bilinear interpolation, and bicubic interpolation were all used to determine the values at rotated grid points. A decomposition of the rotation transformation matrix into three components was also investigated. Linear and cubic interpolation were used in the three pass method. For all of the methods, mean normalized square errors were computed as was a measure of smoothness. Our results demonstrate the trade-offs associated with the different methods and suggest that the method of shears with cubic interpolation offers a computationally efficient approach with accuracy between that of bilinear and bicubic interpolation.