Dual-energy CT technology and spectrum estimation algorithm

Liyuan Chen; Bin Li; Yongqing Li

doi:10.1117/12.2524098

11 May 2019 Dual-energy CT technology and spectrum estimation algorithm

Liyuan Chen, Bin Li, Yongqing Li

Proceedings Volume 11068, Second Symposium on Novel Technology of X-Ray Imaging; 1106810 (2019) https://doi.org/10.1117/12.2524098
Event: Second Symposium on Novel Technology of X-Ray Imaging, 2018, Hefei, China

Abstract

Dual-energy X-ray computed tomography (DECT) can accurately reconstruct the effective atomic number and electron density distribution. Currently, it is an effective material identification technology for security inspections. X-ray energy spectrum estimation plays an important role in dual-energy CT. First, the reconstruction principle of dual-energy CT is introduced. Second, based on common transmission data estimation method, this paper focuses on spectrum estimation using CT image reprojection technology. By scanning the cylindrical model which contain two materials and using the maximum expectation algorithm (EM) to estimate the X-ray energy spectrum distribution, the simulated spectrum of Monte Carlo was used as the initial value of iteration. This method improves the accuracy of dual-energy CT reconstruction algorithm.

Citation Download Citation

Liyuan Chen, Bin Li, and Yongqing Li "Dual-energy CT technology and spectrum estimation algorithm", Proc. SPIE 11068, Second Symposium on Novel Technology of X-Ray Imaging, 1106810 (11 May 2019); https://doi.org/10.1117/12.2524098

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available