Image-Domain Based Material Decomposition by Multi-Constraint Optimization for Spectral CT

IEEE Access

Shaoyu Wang¹  Haijun Yu¹  Jian Feng²  Fenglin Liu² 

[1] Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Ministry of Education, Chongqing University, Chongqing, China;Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing, China;

关键词: Spectral CT;    image-domain;    multi-constraint optimization;    material decomposition;   

DOI  :  10.1109/ACCESS.2020.3016675

来源: DOAJ

【 摘 要 】

As a new generation computed tomography (CT) technology, spectral CT has great potential in many aspects, especially in the identification and decomposition of materials. To achieve higher accuracy of materials decomposition, we propose a multi-constraint based nonlocal total variation (NLTV) method, named as MCNLTV. Because image-domain based material decomposition belongs to the two-step material decomposition method, the Filter Back-Projection (FBP) algorithm or SART algorithm is used to reconstruct spectral CT images in the first step. Then the material attenuation coefficient matrix is obtained from the reconstruction results. In the second step, MCNLTV regularization is utilized to obtain the material decomposition image. Both simulation experiments and real data experiments are carried out. Experiment results show that the proposed method can obtain higher accuracy of material decomposition than traditional total variation based material decomposition (TVMD), ROF-LLT regularization and direct inverse transformation (DI) for spectral CT.

【 授权许可】

Unknown