Paper Info
Title
Polychromatic Iterative Statistical Material Image Reconstruction For Photon-Counting Computed Tomography
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.
Year
DOI
Venue
2016
10.1155/2016/5871604
INTERNATIONAL JOURNAL OF BIOMEDICAL IMAGING
Field
DocType
Volume
Photon counting,Iterative reconstruction,Convergence (routing),Computer vision,Computer science,Charge sharing,Image noise,Regularization (mathematics),Pixel,Artificial intelligence,Logarithm
Journal
2016
ISSN
Citations 
PageRank 
1687-4188
3
0.71
References 
Authors
2
5
Name
Order
Citations
PageRank
Thomas Weidinger130.71
Thorsten M. Buzug251881.29
Thomas Flohr36815.47
Steffen Kappler442.11
karl stierstorfer583.96