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ICASSP
2011
IEEE

Sparsity-based Sinogram Denoising for low-dose Computed Tomography

10 years 1 months ago
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.
Joseph Shtok, Michael Elad, Michael Zibulevsky
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Joseph Shtok, Michael Elad, Michael Zibulevsky
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