Sparsity-based Sinogram Denoising for low-dose Computed Tomography

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 a 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 preserve low-contrast edges for visibility of soft tissues. 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.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Joseph Shtok, Michael Elad, Michael Zibulevsky
• Keywords: Error Measure, ICASSP 2011, Off-line Learning Process, Patch-wise Non-linear Processing, Signal Processing