Adaptive noise correction of dual-energy computed tomography images

Rafael Simon Maia, Christian Jacob, Amy K. Hara, Alvin C Silva, William Pavlicek, Joseph Ross Mitchell

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Noise reduction in material density images is a necessary preprocessing step for the correct interpretation of dual-energy computed tomography (DECT) images. In this paper we describe a new method based on a local adaptive processing to reduce noise in DECT images Methods: An adaptive neighborhood Wiener (ANW) filter was implemented and customized to use local characteristics of material density images. The ANW filter employs a three-level wavelet approach, combined with the application of an anisotropic diffusion filter. Material density images and virtual monochromatic images are noise corrected with two resulting noise maps. Results: The algorithm was applied and quantitatively evaluated in a set of 36 images. From that set of images, three are shown here, and nine more are shown in the online supplementary material. Processed images had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than the raw material density images. The average improvements in SNR and CNR for the material density images were 56.5 and 54.75 %, respectively. Conclusion: We developed a new DECT noise reduction algorithm. We demonstrate throughout a series of quantitative analyses that the algorithm improves the quality of material density images and virtual monochromatic images.

Original languageEnglish (US)
JournalInternational journal of computer assisted radiology and surgery
DOIs
StateAccepted/In press - Oct 13 2015

Keywords

  • Adaptive Wiener filter
  • Dual-energy computed tomography
  • Material density
  • Noise reduction

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Surgery

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