Quadratic Regularization Design for Iterative Reconstruction in 3D multi-slice Axial CT

Abstract

In X-ray CT, statistical methods for tomographic image reconstruction create images with better noise properties than conventional filtered back projection (FBP) techniques. Penalized-likelihood (PL) image reconstruction methods maximize an objective function based on the log-likelihood of sinogram measurements and on a user defined roughness penalty which controls noise. Penalized-likelihood methods (as well as penalized weighted least squares methods) based on conventional quadratic regularizers result in nonuniform and anisotropic spatial resolution. We have previously addressed this problem for 2D emission tomography, 2D fan-beam transmission tomography, and 3D cylindrical emission tomography. This paper extends those methods to 3D multi-slice axial CT with small cone angles.