SPARSITY-BASED SINOGRAM DENOISING FOR LOW-DOSE COMPUTED TOMOGRAPHY
Presented by: Joseph Shtok, Author(s): Joseph Shtok, Michael Elad, Michael Zibulevsky, Technion / Israel Institute of Technology, Israel
We propose a sinogram restoration method which consists of a patch-wise non-linear processing, based on a sparsity prior in terms of a learned dictionary. An off-line learning process uses the statistical model of the sinogram noise and minimizes an error measure in the image domain over the training set. The error measure is designed to improve the reconstruction of low-contrast image edges. Our numerical study shows that the algorithm improves on the performance of the standard Filtered Back-Projection algorithm and effectively allows to halve the radiation dose for the same image quality.