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14 February 2012Design of spectral filtering for tissue classification
Tissue characterization from imaging studies is an integral part of clinical practice. We describe a spectral filter
design for tissue separation in dual energy CT scans obtained from Gemstone Spectral Imaging scanner. It
enables to have better 2D/3D visualization and tissue characterization in normal and pathological conditions.
The major challenge to classify tissues in conventional computed tomography (CT) is the x-ray attenuation
proximity of multiple tissues at any given energy. The proposed method analyzes the monochromatic images
at different energy levels, which are derived from the two scans obtained at low and high KVp through fast
switching. Although materials have a distinct attenuation profile across different energies, tissue separation
is not trivial as tissues are a mixture of different materials with range of densities that vary across subjects.
To address this problem, we define spectral filtering, that generates probability maps for each tissue in multi-energy
space. The filter design incorporates variations in the tissue due to composition, density of individual
constituents and their mixing proportions. In addition, it also provides a framework to incorporate zero mean
Gaussian noise. We demonstrate the application of spectral filtering for bone-free vascular visualization and
calcification characterization.