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Split-Bregman-based sparse-view CT reconstruction

Bert Vandeghinste (UGent) , Bart Goossens (UGent) , Jan De Beenhouwer (UGent) , Aleksandra Pizurica (UGent) , Wilfried Philips (UGent) , Stefaan Vandenberghe (UGent) and Steven Staelens (UGent)
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Abstract
Total variation minimization has been extensively researched for image denoising and sparse view reconstruction. These methods show superior denoising performance for simple images with little texture, but result in texture information loss when applied to more complex images. It could thus be beneficial to use other regularizers within medical imaging. We propose a general regularization method, based on a split-Bregman approach. We show results for this framework combined with a total variation denoising operator, in comparison to ASD-POCS. We show that sparse-view reconstruction and noise regularization is possible. This general method will allow us to investigate other regularizers in the context of regularized CT reconstruction, and decrease the acquisition times in µCT.
Keywords
Noise, Computed Tomography, Reconstruction Algorithms, Iterative Algorithms

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Chicago
Vandeghinste, Bert, Bart Goossens, Jan De Beenhouwer, Aleksandra Pizurica, Wilfried Philips, Stefaan Vandenberghe, and Steven Staelens. 2011. “Split-Bregman-based Sparse-view CT Reconstruction.” In Fully 3D 2011 Proceedings, 431–434.
APA
Vandeghinste, B., Goossens, B., De Beenhouwer, J., Pizurica, A., Philips, W., Vandenberghe, S., & Staelens, S. (2011). Split-Bregman-based sparse-view CT reconstruction. Fully 3D 2011 proceedings (pp. 431–434). Presented at the 11th International meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully 3D 11).
Vancouver
1.
Vandeghinste B, Goossens B, De Beenhouwer J, Pizurica A, Philips W, Vandenberghe S, et al. Split-Bregman-based sparse-view CT reconstruction. Fully 3D 2011 proceedings. 2011. p. 431–4.
MLA
Vandeghinste, Bert, Bart Goossens, Jan De Beenhouwer, et al. “Split-Bregman-based Sparse-view CT Reconstruction.” Fully 3D 2011 Proceedings. 2011. 431–434. Print.
@inproceedings{1957394,
  abstract     = {Total variation minimization has been extensively researched for image denoising and sparse view reconstruction. These methods show superior denoising performance for simple images with little texture, but result in texture information loss when applied to more complex images. It could thus be beneficial to use other regularizers within medical imaging. We propose a general regularization method, based on a split-Bregman approach. We show results for this framework combined with a total variation denoising operator, in comparison to ASD-POCS. We show that sparse-view reconstruction and noise regularization is possible. This general method will allow us to investigate other regularizers in the context of regularized CT reconstruction, and decrease the acquisition times in {\textmu}CT.},
  author       = {Vandeghinste, Bert and Goossens, Bart and De Beenhouwer, Jan and Pizurica, Aleksandra and Philips, Wilfried and Vandenberghe, Stefaan and Staelens, Steven},
  booktitle    = {Fully 3D 2011 proceedings},
  keyword      = {Noise,Computed Tomography,Reconstruction Algorithms,Iterative Algorithms},
  language     = {eng},
  location     = {Potsdam, Germany},
  pages        = {431--434},
  title        = {Split-Bregman-based sparse-view CT reconstruction},
  year         = {2011},
}