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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Communication dans un congrès
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, May 2011, Praha, Czech Republic. pp.569 -572, 2011, 〈10.1109/ICASSP.2011.5946467〉
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https://hal.inria.fr/hal-00700360
Contributeur : Jules Espiau de Lamaestre <>
Soumis le : mardi 22 mai 2012 - 17:17:37
Dernière modification le : jeudi 9 juillet 2015 - 14:42:31

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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, 2011, 〈10.1109/ICASSP.2011.5946467〉. 〈hal-00700360〉

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