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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)
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Ghent researchers on unfolded proteins in inflammatory disease (GROUP-ID)
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Ghent researchers on unfolded proteins in inflammatory disease (GROUP-ID)
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.
Keywords
Shearlets, Noise regularization, CT regularization, Shearlet-based regularization, Total Variation

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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.
@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},
}

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