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Polychromatic Iterative Statistical Material Image Reconstruction for Photon-counting Computed Tomography


Affiliations
1 Institute of Medical Engineering, University of Lubeck, Ratzeburger Allee 160, 23538 Lubeck, Germany
2 Siemens AG, Healthcare Sector, Imaging & Therapy Division, Siemensstrasse 1, 91301 Forchheim, Germany
 

This work proposes a dedicated statistical algorithm to perform a direct reconstruction of material-decomposed images from data acquired with photon-counting detectors (PCDs) in computed tomography. It is based on local approximations (surrogates) of the negative logarithmic Poisson probability function. Exploiting the convexity of this function allows for parallel updates of all image pixels. Parallel updates can compensate for the rather slow convergence that is intrinsic to statistical algorithms.We investigate the accuracy of the algorithm for ideal photon-counting detectors. Complementarily, we apply the algorithm to simulation data of a realistic PCD with its spectral resolution limited by K-escape, charge sharing, and pulse-pileup. For data from both an ideal and realistic PCD, the proposed algorithm is able to correct beam-hardening artifacts and quantitatively determine the material fractions of the chosen basis materials. Via regularization we were able to achieve a reduction of image noise for the realistic PCD that is up to 90% lower compared to material images form a linear, image-based material decomposition using FBP images. Additionally, we find a dependence of the algorithms convergence speed on the threshold selection within the PCD.
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  • Polychromatic Iterative Statistical Material Image Reconstruction for Photon-counting Computed Tomography

Abstract Views: 98  |  PDF Views: 2

Authors

Thomas Weidinger
Institute of Medical Engineering, University of Lubeck, Ratzeburger Allee 160, 23538 Lubeck, Germany
Thorsten M. Buzug
Institute of Medical Engineering, University of Lubeck, Ratzeburger Allee 160, 23538 Lubeck, Germany
Thomas Flohr
Siemens AG, Healthcare Sector, Imaging & Therapy Division, Siemensstrasse 1, 91301 Forchheim, Germany
Steffen Kappler
Siemens AG, Healthcare Sector, Imaging & Therapy Division, Siemensstrasse 1, 91301 Forchheim, Germany
Karl Stierstorfer
Siemens AG, Healthcare Sector, Imaging & Therapy Division, Siemensstrasse 1, 91301 Forchheim, Germany

Abstract


This work proposes a dedicated statistical algorithm to perform a direct reconstruction of material-decomposed images from data acquired with photon-counting detectors (PCDs) in computed tomography. It is based on local approximations (surrogates) of the negative logarithmic Poisson probability function. Exploiting the convexity of this function allows for parallel updates of all image pixels. Parallel updates can compensate for the rather slow convergence that is intrinsic to statistical algorithms.We investigate the accuracy of the algorithm for ideal photon-counting detectors. Complementarily, we apply the algorithm to simulation data of a realistic PCD with its spectral resolution limited by K-escape, charge sharing, and pulse-pileup. For data from both an ideal and realistic PCD, the proposed algorithm is able to correct beam-hardening artifacts and quantitatively determine the material fractions of the chosen basis materials. Via regularization we were able to achieve a reduction of image noise for the realistic PCD that is up to 90% lower compared to material images form a linear, image-based material decomposition using FBP images. Additionally, we find a dependence of the algorithms convergence speed on the threshold selection within the PCD.