NON-LINEAR 3D RECONSTRUCTION FOR COMPRESSIVE X-RAY TOMOSYNTHESIS

被引:0
作者
Zhao, Qile [1 ]
Ma, Xu [1 ]
Cuadros, Angela [2 ]
Arce, Gonzalo R. [2 ]
Chen, Rui [3 ]
机构
[1] Beijing Inst Technol, Sch Opt & Photon, Minist Educ China, Key Lab Photoelect Imaging Technol & Syst, Beijing 100081, Peoples R China
[2] Univ Delaware, Dept Elect & Comp Engn, Newark, DE 19716 USA
[3] Chinese Acad Sci, Inst Microelect, Integrated Circuit Adv Proc Ctr, Beijing 100029, Peoples R China
来源
2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2020年
基金
美国国家科学基金会;
关键词
X-ray tomosynthesis; non-linear reconstruction; compressed sensing; coding mask; OPTIMIZATION; ALGORITHM;
D O I
暂无
中图分类号
TB8 [摄影技术];
学科分类号
0804 ;
摘要
Compressive X-ray tomosynthesis is an emerging technology that can be used for medical diagnosis, safety inspection and industrial non-destructive testing. Traditional compressive X-ray tomosynthesis uses sequential illumination to interrogate the object of interest, which generates non-overlapping projection measurements. However, when multiple X-ray sources emit simultaneously, the projections corresponding to different sources overlap creating multiplexed measurements. Such measurement strategy leads to a non-linear reconstruction problem. The reconstruction of the three-dimensional (3D) object from multiplexed projections is an important problem. This paper proposes a non-linear 3D reconstruction approach for compressive X-ray tomosynthesis, where a set of coding masks are used to generate structured illumination and reduce radiation dose. The effectiveness of the method is verified by simulation experiments. It is shown that the proposed approach can reconstruct high-quality images with just a few snapshots.
引用
收藏
页码:3149 / 3153
页数:5
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