Paper
1 March 2019 An improved physics model for multi-material identification in photon counting CT
Xu Dong, Olga V. Pen, Zhicheng Zhang, Guohua Cao
Author Affiliations +
Abstract
Photon-counting computed tomography (PCCT) with energy discrimination capabilities hold great potentials to improve the limitations of the conventional CT, including better signal-to-noise ratio (SNR), improved contrast-to-noise ratio (CNR), lower radiation dose, and most importantly, simultaneous multiple material identification. One potential way of material identification is via calculation of effective atomic number (Zeff) and effective electron density (peeff) from PCCT image data. However, the current methods for calculating effective atomic number and effective electron density from PCCT image data are mostly based on semi-empirical models and accordingly are not sufficiently accurate. Here, we present a physics-based model to calculate the effective atomic number and effective electron density of various matters, including single element substances, molecular compounds, and multi-material mixtures as well. The model was validated over several materials under various combinations of energy bins. A PCCT system was simulated to generate the PCCT image data, and the proposed model was applied to the PCCT image data. Our model yielded a relative standard deviations for effective atomic numbers and effective electron densities at less than 1%. Our results further showed that five different materials can be simultaneously identified and well separated in a Zeff − peeff map. The model could serve as a basis for simultaneous material identification from PCCT.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xu Dong, Olga V. Pen, Zhicheng Zhang, and Guohua Cao "An improved physics model for multi-material identification in photon counting CT", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109484O (1 March 2019); https://doi.org/10.1117/12.2512525
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling

Sensors

X-rays

Signal attenuation

Physics

Photon counting

Chemical elements

Back to Top