Direct adaptive algorithms for CT reconstruction

This work concerns with linear and spatially-adaptive direct reconstruction algorithms for 2-D parallel-beam transmission tomography, extending the filtered back-projection (FBP). The standard apodized Ram-Lak filter kernel is replaced with a bank of statistically trained 2-D convolution kernels, leading to improved reconstruction results. Two types of filter training procedures are considered. The first deals with reconstruction from noisy and truncated projections in a predefined region of interest, for images from a known family. In the second algorithm, termed SPADES, the training aims at improving the impulse response properties of the overall projection-reconstruction scheme. In this algorithm, the degree of smoothing applied to the reconstructed image is spatially controlled by a switch rule. Both methods are shown by simulations to operate well and lead to substantially improved reconstruction results.