Sparsity-based Sinogram Denoising for low-dose Computed Tomography

J. Shtok 1 Michael Elad 1 Michael Zibulevsky 1
1 Department of Computer Science [Haifa]
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.
Keywords : computed tomography filtered backprojection algorithm image quality off-line learning patch-wise nonlinear processing radiation dose sinogram restoration method soft tissues sparsity-based sinogram denoising statistical model biological effects of radiation computerised tomography image restoration learning (artificial intelligence) statistical analysis
Type de document :
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〉
Liste complète des métadonnées
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

Lien texte intégral

openaccess

Identifiants

Collections

Citation

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〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

266

Contact


archive-ouverte@inria.fr