Literature DB >> 29130074

Does Noise Weighting Matter in CT Iterative Reconstruction?

Gengsheng L Zeng1, Wenli Wang2.   

Abstract

This paper uses a computer simulation to investigate whether a more accurate noise model always results in less noisy images in CT iterative reconstruction. We start with a hypothetic non-realistic noise model for the CT measurements, by assuming that the attenuation coefficient is energy independent and there is no scattering. A variance formula for this model is derived and presented. Based on this model, computer simulations are conducted with 12 different ad hoc noise weighting methods, and their results are compared. The simple Poisson noise model performs better than other more accurate models, when the projection data are generated with the hypothetical noise model. A more accurate noise model does not necessarily produce a less-noisy image. In this counter example, modeling the system's electronic noise during reconstruction does not help reducing the image noise. A simpler noise model sometimes can outperform the complicated and more accurate noise model.

Entities:  

Keywords:  Image reconstruction; Poisson distribution; X-ray CT; noise model

Year:  2016        PMID: 29130074      PMCID: PMC5675719          DOI: 10.1109/TNS.2016.2630685

Source DB:  PubMed          Journal:  IEEE Trans Radiat Plasma Med Sci        ISSN: 2469-7303


  7 in total

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  7 in total
  2 in total

1.  A Task-dependent Investigation on Dose and Texture in CT Image Reconstruction.

Authors:  Yongfeng Gao; Zhengrong Liang; Hao Zhang; Jie Yang; John Ferretti; Thomas Bilfinger; Kavitha Yaddanapudi; Mark Schweitzer; Priya Bhattacharji; William Moore
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2019-12-04

2.  Investigation of Low-Dose CT Image Denoising Using Unpaired Deep Learning Methods.

Authors:  Zeheng Li; Shiwei Zhou; Junzhou Huang; Lifeng Yu; Mingwu Jin
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2020-07-07
  2 in total

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