Skip to Main content Skip to Navigation
Conference papers

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
Document type :
Conference papers
Complete list of metadata
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

Identifiers

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, ⟨10.1109/ICASSP.2011.5946467⟩. ⟨hal-00700360⟩

Export

BibTeX TEI DC DCterms EndNote Datacite

Share

Metrics

Record views

90

 

Contact                    Legal Notice                    Personal Data