A dynamic CT image reconstruction method by inducing prior information from PCA analysis

Xun Jia, Yifei Lou, Ruijiang Li, Xuejun Gu, John Levis, Steve Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Under-sampling and insufficient data result in a big challenge in the reconstruction of x-ray computed tomographic (CT) images. In addition, patient's respiratory motion also deteriorates this reconstruction process as it normally leads to blurred outputs. In this work, we propose an iterative method with a combination of total variation (TV) regularization and principle component analysis (PCA) regularization. Partial prior knowledge of the CT images, obtained through PCA analysis of training images is incorporated in the reconstruction process. Numerical experiments are performed in the context of a fan-beam CT reconstruction, which shows advantages of our method over the ones with just TV regularization or just PCA regularization.

Original languageEnglish (US)
Title of host publication8th International Conference on Machine Learning and Applications, ICMLA 2009
Pages473-477
Number of pages5
DOIs
StatePublished - Dec 1 2009
Event8th International Conference on Machine Learning and Applications, ICMLA 2009 - Miami Beach, FL, United States
Duration: Dec 13 2009Dec 15 2009

Publication series

Name8th International Conference on Machine Learning and Applications, ICMLA 2009

Other

Other8th International Conference on Machine Learning and Applications, ICMLA 2009
CountryUnited States
CityMiami Beach, FL
Period12/13/0912/15/09

Keywords

  • Computed tomography
  • Image reconstruction
  • PCA
  • TV norm

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction
  • Software

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  • Cite this

    Jia, X., Lou, Y., Li, R., Gu, X., Levis, J., & Jiang, S. (2009). A dynamic CT image reconstruction method by inducing prior information from PCA analysis. In 8th International Conference on Machine Learning and Applications, ICMLA 2009 (pp. 473-477). [5381457] (8th International Conference on Machine Learning and Applications, ICMLA 2009). https://doi.org/10.1109/ICMLA.2009.77