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CT Statistical and Iterative Reconstructions and Post Processing

  • Norbert J. PelcEmail author
  • Adam Wang
Chapter

Abstract

For decades, CT images were reconstructed from the measured raw data using analytical reconstruction methods, such as filtered backprojection (FBP). While FBP is fast and accurate, the measured data are rarely ideal, and iterative and statistical methods can provide significant benefit particularly when the data quality is poor. They can produce images with lower noise and can also reduce artifacts from system imperfections. However, the resulting images are nonlinear and non-stationary and have other properties that are different from those produced by FBP that should be kept in mind when the images are interpreted.

Keywords

Image reconstruction Filtered backprojection Iterative reconstruction Statistical reconstruction Edge-preserving smoothing 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Bioengineering and RadiologyStanford UniversityStanfordUSA
  2. 2.Department of RadiologyStanford UniversityStanfordUSA

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