Table of Contents Author Guidelines Submit a Manuscript
Corrigendum

A corrigendum for this article has been published. To view the corrigendum, please click here.

International Journal of Biomedical Imaging
Volume 2016, Article ID 5871604, 15 pages
http://dx.doi.org/10.1155/2016/5871604
Research Article

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

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

Received 29 November 2015; Revised 5 February 2016; Accepted 15 February 2016

Academic Editor: Jiang Hsieh

Copyright © 2016 Thomas Weidinger et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.