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Image-based noise reduction for material decomposition in dual or multi energy computed tomography
Author(s): Mahmut Özdemir; Sabrina Dorn; Francesco Pisana; Monika Uhrig; Heinz-Peter Schlemmer M.D.; Marc Kachelrieß
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Paper Abstract

Clinical dual energy computed tomography (DECT) scanners have a material decomposition application to display the contrast-enhanced computed tomography (CT) scan as if it were scanned without contrast agent: virtual-non-contrast (VNC) imaging. The clinical benefit of VNC imaging can potentially be increased using photon counting detector-based multi energy CT (MECT) scanners. Furthermore, dose efficiency and contrast- to-noise ratio (CNR) may be improved in MECT. Effectively, the material decomposition can be performed in image domain. However, material decomposition increases the noise of the material images. Therefore, we generalized an image filter to achieve less noisy decomposed material images. The image-based noise reduction for the material images can be achieved by adding the highpass of the CNR optimized energy image to the lowpass filtered material image. In this way, the image-based noise reduction has the potential to recover some subtle structures that are less visible in the unfiltered images. In this study, we generalize the measurement-dependent filter of Macovski et al. to the case of MECT. The method is performed using phantom measurements from the Siemens SOMATOM Definition Flash scanner in single energy scan mode at tube voltages 80 kV, 100 kV, 120 kV and 140 kV to mimic 4 energy bins of a photon counting CT. Using the image-based noise reduction, a factor of 4 noise reduction in the material images can be achieved.

Paper Details

Date Published: 1 March 2019
PDF: 9 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109482U (1 March 2019); doi: 10.1117/12.2513388
Show Author Affiliations
Mahmut Özdemir, Deutsches Krebsforschungszentrum (Germany)
Sabrina Dorn, Deutsches Krebsforschungszentrum (Germany)
Francesco Pisana, Deutsches Krebsforschungszentrum (Germany)
Monika Uhrig, Deutsches Krebsforschungszentrum (Germany)
Heinz-Peter Schlemmer M.D., Deutsches Krebsforschungszentrum (Germany)
Marc Kachelrieß, Deutsches Krebsforschungszentrum (Germany)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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