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Sparsity-based Sinogram Denoising for low-dose Computed Tomography

Abstract : 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.
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https://hal.inria.fr/hal-00700360
Contributor : Jules Espiau De Lamaestre Connect in order to contact the contributor
Submitted on : Tuesday, May 22, 2012 - 5:17:37 PM
Last modification on : Thursday, July 9, 2015 - 2:42:31 PM

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J. Shtok, Michael Elad, Michael Zibulevsky. Sparsity-based Sinogram Denoising for low-dose Computed Tomography. Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, May 2011, Praha, Czech Republic. pp.569 -572, ⟨10.1109/ICASSP.2011.5946467⟩. ⟨hal-00700360⟩

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