Presentation + Paper
1 March 2019 Simulation model for evaluating energy-resolving photon-counting CT detectors based on generalized linear-systems framework
Mats Persson, Norbert J. Pelc
Author Affiliations +
Abstract
Photon counting detectors are interesting candidates for next-generation clinical computed tomography scanners, promising improved contrast-to-noise ratio, spatial resolution and energy information compared to conventional energy-integrating detectors. Most attention is focused on cadmium telluride (CdTe) (or CZT) detectors, but silicon (Si) has been proposed as an alternative. We present detector simulation models fitted to published spectral response data for CdTe and Si, and use linear-systems theory to evaluate the spatial-frequency dependent DQE for lesion quantification and detection. Our fitted spectral response is consistent with Gaussian charge clouds with σ = 20.5 µm independent of energy for CdTe, and σ = 17 µm at 60 keV with an energy dependence of E0.54 for Si. For a silicon strip detector with 0.5 × 0.5 mm2 pixels separated by a 1D grid of 20 µm tungsten foils, the zero-frequency DQE for iodine detection is 0.43 for 30 mm detector absorption length and 0.46 for 60 mm detector absorption length. For iodine quantification in a water-iodine decomposition, the DQE is 0.26 for 30 mm and 0.27 for 60 mm Si. Compared to this detector, the DQE of a 1.6 mm thick CdTe detector with 0.225 mm pixels and two energy bins is 11-36 % higher for water and iodine detection but 28-51 % lower for material quantification. The predicted performance of Si is competitive with CdTe, suggesting that further consideration is warranted.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mats Persson and Norbert J. Pelc "Simulation model for evaluating energy-resolving photon-counting CT detectors based on generalized linear-systems framework", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109481V (1 March 2019); https://doi.org/10.1117/12.2512593
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CITATIONS
Cited by 5 scholarly publications.
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KEYWORDS
Sensors

Silicon

Monte Carlo methods

Iodine

Tungsten

Absorption

Data modeling

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