Noise Reduction in Medical DECT Data

Information

• Publication Type: Bachelor Thesis
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: February 2014
• First Supervisor: Eduard Gröller

Abstract

Dual energy computed tomography (DECT) recently gained popularity for medical diagnostic imaging. It has been demonstrated how DECT can improve density measurement and material differentiation, and practical applications for DECT imaging in medicine. Noise reduction is standard operation in the process of image enhancement which is necessary operation prior to image evaluation done by radiologist. In this work, we describe two approaches for noise reduction using DECT data. First, we show in the work that the cross or joint bilateral filter can be effectively used on DECT images to reduce noise while preserving edges. Second, noise in two DECT images is anti-correlated and can be effectively removed by the KCNR algorithm. Even better results can be achieved by using algorithms that exploit an additional characteristic information of DECT data, such as the spectral information. It was shown that the KCNR can increase its performance regarding quality when the spectral information is corrected before applying the KCNR. AngioVis framework provides ability to present and manipulate CT data. All discussed image enhancement algorithms are implemented in AngioVis as a plugin.

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BibTeX

@bachelorsthesis{filipovic-2014-dect,
  title =      "Noise Reduction in Medical DECT Data",
  author =     "Mirza Filipovic",
  year =       "2014",
  abstract =   "Dual energy computed tomography (DECT) recently gained
               popularity for medical diagnostic imaging. It has been
               demonstrated how DECT can improve density measurement and
               material differentiation, and practical applications for
               DECT imaging in medicine. Noise reduction is standard
               operation in the process of image enhancement which is
               necessary operation prior to image evaluation done by
               radiologist. In this work, we describe two approaches for
               noise reduction using DECT data. First, we show in the work
               that the cross or joint bilateral filter can be effectively
               used on DECT images to reduce noise while preserving edges.
               Second, noise in two DECT images is anti-correlated and can
               be effectively removed by the KCNR algorithm. Even better
               results can be achieved by using algorithms that exploit an
               additional characteristic information of DECT data, such as
               the spectral information. It was shown that the KCNR can
               increase its performance regarding quality when the spectral
               information is corrected before applying the KCNR. AngioVis
               framework provides ability to present and manipulate CT
               data. All discussed image enhancement algorithms are
               implemented in AngioVis as a plugin.",
  month =      feb,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2014/filipovic-2014-dect/",
}