Simultaneous Imaging of Bio- and Non-Conductive Targets by Combining Frequency and Time Difference Imaging Methods in Electrical Impedance Tomography

被引:9
|
作者
Bai, Xue [1 ]
Liu, Dun [2 ]
Wei, Jinzhao [1 ]
Bai, Xu [1 ]
Sun, Shijie [1 ,2 ]
Tian, Wenbin [3 ]
机构
[1] Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing 100191, Peoples R China
[2] Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
[3] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China
来源
BIOSENSORS-BASEL | 2021年 / 11卷 / 06期
关键词
electrical impedance tomography; frequency difference; time difference; lung imaging; MULTIFREQUENCY EIT SYSTEM; RECONSTRUCTION ALGORITHMS; DIELECTRIC-PROPERTIES; BIOLOGICAL TISSUES;
D O I
10.3390/bios11060176
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
As a promising medical imaging modality, electrical impedance tomography (EIT) can image the electrical properties within a region of interest using electrical measurements applied at electrodes on the region boundary. This paper proposes to combine frequency and time difference imaging methods in EIT to simultaneously image bio- and non-conductive targets, where the image fusion is accomplished by applying a wavelet-based technique. To enable image fusion, both time and frequency difference imaging methods are investigated regarding the reconstruction of bio- or non-conductive inclusions in the target region at varied excitation frequencies, indicating that none of those two methods can tackle with the scenarios where both bio- and non-conductive inclusions exist. This dilemma can be resolved by fusing the time difference (td) and appropriate frequency difference (fd) EIT images since they are complementary to each other. Through simulation and in vitro experiment, it is demonstrated that the proposed fusion method can reasonably reconstruct both the bio- and non-conductive inclusions within the lung models established to simulate the ventilation process, which is expected to be beneficial for the diagnosis of lung-tissue related diseases by EIT.
引用
收藏
页数:16
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