Spatio-energetic cross-talks in photon counting detectors: Detector model and correlated Poisson data generator

Taguchi, Katsuyuki; Polster, Christoph; Lee, Okkyun und Kappler, Steffen (2016): Spatio-energetic cross-talks in photon counting detectors: Detector model and correlated Poisson data generator. In: Gimi, Barjor und Krol, Andrzej (Hrsg.): Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. Proceedings SPIE, Bd. 9788. SPIE.

Abstract

An x-ray photon interacts with photon counting detectors (PCDs) and generates an electron charge cloud or multiple clouds. The clouds (thus, the photon energy) may be split between two adjacent PCD pixels when the interaction occurs near pixel boundaries, producing a count at both of the two pixels. This is called double-counting with charge sharing. The output of individual PCD pixel is Poisson distributed integer counts;however, the outputs of adjacent pixels are correlated due to double-counting. Major problems are the lack of detector noise model for the spatio-energetic crosstalk and the lack of an efficient simulation tool. Monte Carlo simulation can accurately simulate these phenomena and produce noisy data;however, it is not computationally efficient. In this study, we developed a new detector model and implemented into an efficient software simulator which uses a Poisson random number generator to produce correlated noisy integer counts. The detector model takes the following effects into account effects: (1) detection efficiency and incomplete charge collection;(2) photoelectric effect with total absorption;(3) photoelectric effect with fluorescence x-ray emission and re-absorption;(4) photoelectric effect with fluorescence x-ray emission which leaves PCD completely;and (5) electric noise. The model produced total detector spectrum similar to previous MC simulation data. The model can be used to predict spectrum and correlation with various different settings. The simulated noisy data demonstrated the expected perfoimance: (a) data were integers;(b) the mean and covariance matrix was close to the target values;(c) noisy data generation was very efficient

• Dokumententyp: Buchbeitrag
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 0277-786X
• Sprache: Englisch