Sparse optimization for image reconstruction in Electrical Impedance Tomography

被引：5
|
作者：
Varanasi, Santhosh Kumar [1 ]
Manchikatla, Chaitanya [1 ]
Polisetty, Venkata Goutham [1 ]
Jampana, Phanindra [1 ]
机构：
[1] Indian Inst Technol Hyderabad, Dept Chem Engn, Sangareddy 502285, Telangana, India
来源：
IFAC PAPERSONLINE | 2019年 / 52卷 / 01期
关键词：
Parameter estimation; Electrical Resistance/Impedance Tomography; Sparse optimization; Orthogonal matching pursuit; REGULARIZATION; FLOW;
D O I：
10.1016/j.ifacol.2019.06.033
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Electrical Impedance Tomography (EIT) can be used to obtain phase boundaries and gas holdups in multiphase flows. The main challenge in image reconstruction using EIT is the low spatial resolution. In this paper, a reconstruction algorithm using sparse optimization techniques is presented. For multiphase flows, gradients in the conductivity vector are sparse. Therefore, the reconstruction problem is formulated as identification of this sparse vector given the current-voltage measurements. A new iterative algorithm is proposed to estimate the conductivity values. The accuracy of the proposed method is demonstrated with the help of several examples and comparison with an existing technique. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：34 / 39
页数：6
相关论文
共 50 条
  • [1] Sparse image reconstruction of intracerebral hemorrhage with electrical impedance tomography
    Shi, Yanyan
    Wu, Yuehui
    Wang, Meng
    Tian, Zhiwei
    Kong, Xiaolong
    He, Xiaoyue
    JOURNAL OF MEDICAL IMAGING, 2021, 8 (01)
  • [2] Image Reconstruction Algorithm for Electrical Impedance Tomography Based on Block Sparse Bayesian Learning
    Liu, Shengheng
    Jia, Jiabin
    Yang, Yunjie
    2017 IEEE INTERNATIONAL CONFERENCE ON IMAGING SYSTEMS AND TECHNIQUES (IST), 2017, : 267 - 271
  • [3] Temporal image reconstruction in electrical impedance tomography
    Adler, Andy
    Dai, Tao
    Lionheart, William R. B.
    PHYSIOLOGICAL MEASUREMENT, 2007, 28 (07) : S1 - S11
  • [4] Patch-based sparse reconstruction for electrical impedance tomography
    Wang, Qi
    Zhang, Pengcheng
    Wang, Jianming
    Chen, Qingliang
    Lian, Zhijie
    Li, Xiuyan
    Sun, Yukuan
    Duan, Xiaojie
    Cui, Ziqiang
    Sun, Benyuan
    Wang, Huaxiang
    SENSOR REVIEW, 2017, 37 (03) : 257 - 269
  • [5] Image Reconstruction in Electrical Impedance Tomography Based on Structure-Aware Sparse Bayesian Learning
    Liu, Shengheng
    Jia, Jiabin
    Zhang, Yimin D.
    Yang, Yunjie
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2018, 37 (09) : 2090 - 2102
  • [6] Logistic regression in image reconstruction in electrical impedance tomography
    Kozlowski, Edward
    Rymarczyk, Tomasz
    Klosowski, Grzegorz
    Cieplak, Tomasz
    PRZEGLAD ELEKTROTECHNICZNY, 2020, 96 (05): : 95 - 98
  • [7] New regularized image reconstruction for electrical impedance tomography
    Hou, WD
    Mo, YL
    IMAGE MATCHING AND ANALYSIS, 2001, 4552 : 286 - 291
  • [8] Influence of regularization in image reconstruction in electrical impedance tomography
    Queiroz, J. L. L.
    FIRST LATIN-AMERICAN CONFERENCE ON BIOIMPEDANCE (CLABIO 2012), 2012, 407
  • [9] Split Bregman iterative algorithm for sparse reconstruction of electrical impedance tomography
    Wang, Jing
    Ma, Jianwei
    Han, Bo
    Li, Qin
    SIGNAL PROCESSING, 2012, 92 (12) : 2952 - 2961
  • [10] Image Reconstruction for Electrical Capacitance Tomography Based on Sparse Representation
    Ye, Jiamin
    Wang, Haigang
    Yang, Wuqiang
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2015, 64 (01) : 89 - 102
12345