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Sensing and Imaging

, 20:12 | Cite as

Projection Data Smoothing for Low-Dose CT Based on \(\ell _p\) Regularization

  • Xiaojuan Deng
  • Xuehong Liu
  • Hongwei LiEmail author
Original Paper
  • 13 Downloads
Part of the following topical collections:
  1. Recent Developments in Sensing and Imaging

Abstract

Projection data smoothing is a traditional technique for low-dose computed tomography. The projection data can be modeled as a piecewise smooth function. It’s well known that \(\ell _1\) regularization of the image gradients tries to recover piecewise constant functions, while \(\ell _2\) regularization recovers smooth functions. This motivates us to propose the \(\ell _p\) regularization with \(1<p<2\) for low-dose projection data smoothing. Besides, the non-stationary Gaussian noise model for the projection data is built into the regularization term. The resulting model is then linearized such that the fast split-Bregman algorithm can be applied. Experiments on simulated projection data as well as real data show that \(\ell _p\) regularization with \(1<p<2\) could achieve better reconstruction compared to \(\ell _{1}\) regularization.

Keywords

Low-dose CT Projection smoothing \(\ell _p\) regularization 

Notes

Acknowledgements

Thanks for the support of the National Natural Science Foundation of China (NSFC) (61871275 and 61771324). And the authors are grateful to Beijing Higher Institution Engineering Research Center of Testing and Imaging as well as Beijing Advanced Innovation Center for Imaging Technology for funding this research work.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mathematical SciencesCapital Normal UniversityBeijingChina
  2. 2.Beijing Advanced Innovation Center for Imaging TechnologyCapital Normal UniversityBeijingChina

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