A method of X-ray source spectrum estimation from transmission measurements based on compressed sensing

• Main Authors: Bin Liu, Hongrun Yang, Huanwen Lv, Lan Li, Xilong Gao, Jianping Zhu, Futing Jing
• Format: Article
• Language: English
• Published: Elsevier 2020-07-01
• Series: Nuclear Engineering and Technology
• Subjects: X-ray source spectrum estimation; Transmission measurements; Compressed sensing and sparse representation
• Online Access: http://www.sciencedirect.com/science/article/pii/S1738573319306370
• ISSN: 1738-5733

A new method of X-ray source spectrum estimation based on compressed sensing is proposed in this paper. The algorithm K-SVD is applied for sparse representation. Nonnegative constraints are added by modifying the L1 reconstruction algorithm proposed by Rosset and Zhu. The estimation method is demonstrated on simulated spectra typical of mammography and CT. X-ray spectra are simulated with the Monte Carlo code Geant4. The proposed method is successfully applied to highly ill conditioned and under determined estimation problems with a good performance of suppressing noises. Results with acceptable accuracies (MSE < 5%) can be obtained with 10% Gaussian white noises added to the simulated experimental data. The biggest difference between the proposed method and the existing methods is that multiple prior knowledge of X-ray spectra can be included in one dictionary, which is meaningful for obtaining the true X-ray spectrum from the measurements.