DocumentCode :
2151835
Title :
Sparsity-based Sinogram Denoising for low-dose Computed Tomography
Author :
Shtok, J. ; Elad, M. ; Zibulevsky, M.
Author_Institution :
Comput. Sci. Dept., Technion - Israel Inst. of Technol., Haifa, Israel
fYear :
2011
fDate :
22-27 May 2011
Firstpage :
569
Lastpage :
572
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 :
biological effects of radiation; computerised tomography; image restoration; learning (artificial intelligence); statistical analysis; 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; Computed tomography; Dictionaries; Image restoration; Noise; Noise measurement; Training; X-ray imaging; Computed Tomography; Sparse-Land paradigm; sinogram restoration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
ISSN :
1520-6149
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2011.5946467
Filename :
5946467
Link To Document :
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