Ghent University Academic Bibliography

Advanced

Iterative CT reconstruction using shearlet-based regularization

Bert Vandeghinste UGent, Bart Goossens UGent, Roel Van Holen UGent, Christian Vanhove UGent, Aleksandra Pizurica UGent, Stefaan Vandenberghe UGent and Steven Staelens UGent (2012) Proceedings of SPIE, the International Society for Optical Engineering. 8313.
abstract
In computerized tomography, it is important to reduce the image noise without increasing the acquisition dose. Extensive research has been done into total variation minimization for image denoising and sparse-view reconstruction. However, TV minimization methods show superior denoising performance for simple images (with little texture), but result in texture information loss when applied to more complex images. Since in medical imaging, we are often confronted with textured images, it might not be beneficial to use TV. Our objective is to find a regularization term outperforming TV for sparse-view reconstruction and image denoising in general. A recent efficient solver was developed for convex problems, based on a split-Bregman approach, able to incorporate regularization terms different from TV. In this work, a proof-of-concept study demonstrates the usage of the discrete shearlet transform as a sparsifying transform within this solver for CT reconstructions. In particular, the regularization term is the 1-norm of the shearlet coefficients. We compared our newly developed shearlet approach to traditional TV on both sparse-view and on low-count simulated and measured preclinical data. Shearlet-based regularization does not outperform TV-based regularization for all datasets. Reconstructed images exhibit small aliasing artifacts in sparse-view reconstruction problems, but show no staircasing effect. This results in a slightly higher resolution than with TV-based regularization.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
Shearlets, Noise regularization, CT regularization, Shearlet-based regularization, Total Variation
in
Proceedings of SPIE, the International Society for Optical Engineering
Proc. SPIE Int. Soc. Opt. Eng.
editor
NJ Pelc, RM Nishikawa and BR Whiting
volume
8313
issue title
Medical imaging 2012 : physics of medical imaging
pages
7 pages
publisher
SPIE, the International Society for Optical Engineering
place of publication
Bellingham, WA, USA
conference name
Medical Imaging 2012 : Physics of medical imaging
conference location
San Diego, CA, USA
conference start
2012-02-05
conference end
2012-02-08
Web of Science type
Proceedings Paper
Web of Science id
000304768000121
ISSN
0277-786X
ISBN
9780819489623
DOI
10.1117/12.911057
project
Ghent researchers on unfolded proteins in inflammatory disease (GROUP-ID)
project
Ghent researchers on unfolded proteins in inflammatory disease (GROUP-ID)
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2118542
handle
http://hdl.handle.net/1854/LU-2118542
date created
2012-05-30 07:27:36
date last changed
2014-05-12 10:59:52
@inproceedings{2118542,
  abstract     = {In computerized tomography, it is important to reduce the image noise without increasing the acquisition dose. Extensive research has been done into total variation minimization for image denoising and sparse-view reconstruction. However, TV minimization methods show superior denoising performance for simple images (with little texture), but result in texture information loss when applied to more complex images. Since in medical imaging, we are often confronted with textured images, it might not be beneficial to use TV. Our objective is to find a regularization term outperforming TV for sparse-view reconstruction and image denoising in general. A recent efficient solver was developed for convex problems, based on a split-Bregman approach, able to incorporate regularization terms different from TV. In this work, a proof-of-concept study demonstrates the usage of the discrete shearlet transform as a sparsifying transform within this solver for CT reconstructions. In particular, the regularization term is the 1-norm of the shearlet coefficients. We compared our newly developed shearlet approach to traditional TV on both sparse-view and on low-count simulated and measured preclinical data. Shearlet-based regularization does not outperform TV-based regularization for all datasets. Reconstructed images exhibit small aliasing artifacts in sparse-view reconstruction problems, but show no staircasing effect. This results in a slightly higher resolution than with TV-based regularization.},
  author       = {Vandeghinste, Bert and Goossens, Bart and Van Holen, Roel and Vanhove, Christian and Pizurica, Aleksandra and Vandenberghe, Stefaan and Staelens, Steven},
  booktitle    = {Proceedings of SPIE, the International Society for Optical Engineering},
  editor       = {Pelc, NJ and Nishikawa, RM and Whiting, BR},
  isbn         = {9780819489623},
  issn         = {0277-786X},
  keyword      = {Shearlets,Noise regularization,CT regularization,Shearlet-based regularization,Total Variation},
  language     = {eng},
  location     = {San Diego, CA, USA},
  pages        = {7},
  publisher    = {SPIE, the International Society for Optical Engineering},
  title        = {Iterative CT reconstruction using shearlet-based regularization},
  url          = {http://dx.doi.org/10.1117/12.911057},
  volume       = {8313},
  year         = {2012},
}

Chicago
Vandeghinste, Bert, Bart Goossens, Roel Van Holen, Christian Vanhove, Aleksandra Pizurica, Stefaan Vandenberghe, and Steven Staelens. 2012. “Iterative CT Reconstruction Using Shearlet-based Regularization.” In Proceedings of SPIE, the International Society for Optical Engineering, ed. NJ Pelc, RM Nishikawa, and BR Whiting. Vol. 8313. Bellingham, WA, USA: SPIE, the International Society for Optical Engineering.
APA
Vandeghinste, B., Goossens, B., Van Holen, R., Vanhove, C., Pizurica, A., Vandenberghe, S., & Staelens, S. (2012). Iterative CT reconstruction using shearlet-based regularization. In N. Pelc, R. Nishikawa, & B. Whiting (Eds.), Proceedings of SPIE, the International Society for Optical Engineering (Vol. 8313). Presented at the Medical Imaging 2012 : Physics of medical imaging, Bellingham, WA, USA: SPIE, the International Society for Optical Engineering.
Vancouver
1.
Vandeghinste B, Goossens B, Van Holen R, Vanhove C, Pizurica A, Vandenberghe S, et al. Iterative CT reconstruction using shearlet-based regularization. In: Pelc N, Nishikawa R, Whiting B, editors. Proceedings of SPIE, the International Society for Optical Engineering. Bellingham, WA, USA: SPIE, the International Society for Optical Engineering; 2012.
MLA
Vandeghinste, Bert, Bart Goossens, Roel Van Holen, et al. “Iterative CT Reconstruction Using Shearlet-based Regularization.” Proceedings of SPIE, the International Society for Optical Engineering. Ed. NJ Pelc, RM Nishikawa, & BR Whiting. Vol. 8313. Bellingham, WA, USA: SPIE, the International Society for Optical Engineering, 2012. Print.