Sinogram noise reduction for low-dose CT by statistics-based nonlinear filters

Jing Wang, Hongbing Lu, Tianfang Li, Zhengrong Liang

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

40 Scopus citations

Abstract

Low-dose CT (computed tomography) sinogram data have been shown to be signal-dependent with an analytical relationship between the sample mean and sample variance. Spatially-invariant low-pass linear filters, such as the Butterworth and Hanning filters, could not adequately handle the data noise and statistics-based nonlinear filters may be an alternative choice, in addition to other choices of minimizing cost functions on the noisy data. Anisotropic diffusion filter and nonlinear Gaussian filters chain (NLGC) are two well-known classes of nonlinear filters based on local statistics for the purpose of edge-preserving noise reduction. These two filters can utilize the noise properties of the low-dose CT sinogram for adaptive noise reduction, but can not incorporate signal correlative information for an optimal regularized solution. Our previously-developed Karhunen-Loève (KL) domain PWLS (penalized weighted least square) minimization considers the signal correlation via the KL strategy and seeks the PWLS cost function minimization for an optimal regularized solution for each KL component, i.e., adaptive to the KL components. This work compared the nonlinear filters with the KL-PWLS framework for low-dose CT application. Furthermore, we investigated the nonlinear filters for post KL-PWLS noise treatment in the sinogram space, where the filters were applied after ramp operation on the KL-PWLS treated sinogram data prior to backprojection operation (for image reconstruction). By both computer simulation and experimental low-dose CT data, the nonlinear filters could not outperform the KL-PWLS framework. The gain of post KL-PWLS edge-preserving noise filtering in the sinogram space is not significant, even the noise has been modulated by the ramp operation.

Original languageEnglish (US)
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
EditorsJ.M. Fitzpatrick, J.M. Reinhardt
Pages2058-2066
Number of pages9
Volume5747
EditionIII
DOIs
StatePublished - 2005
EventMedical Imaging 2005 - Image Processing - San Diego, CA, United States
Duration: Feb 13 2005Feb 17 2005

Other

OtherMedical Imaging 2005 - Image Processing
CountryUnited States
CitySan Diego, CA
Period2/13/052/17/05

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Keywords

  • KL-PWLS
  • Low-dose CT
  • Sinogram noise reduction
  • Statistics-based nonlinear filters
  • Streak artifacts

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Wang, J., Lu, H., Li, T., & Liang, Z. (2005). Sinogram noise reduction for low-dose CT by statistics-based nonlinear filters. In J. M. Fitzpatrick, & J. M. Reinhardt (Eds.), Progress in Biomedical Optics and Imaging - Proceedings of SPIE (III ed., Vol. 5747, pp. 2058-2066). [241] https://doi.org/10.1117/12.595662