Conductivity characteristics of human lung tissues. Issue 1 (27th May 2021)

• Record Type: Journal Article
• Authors: Wang, Yaru; Yue, Shihong; Chen, Jun; Li, Qi
• Is Part Of: International journal of imaging systems and technology. Volume 32:Issue 1(2022)
• Page Start: 178
• Page End: 191
• Publication Date: 2021-05-27
• Subjects: conductivity characteristics -- electrical impedance tomography -- human lung tissue -- impedance spectra -- priori information; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22607
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: In the treatment of lung cancer, bedside monitoring plays an important role. X‐ray computed tomography, as a mostly used technique, cannot provide medical surveillance for lung cancer patients in real‐time. In comparison, electrical impedance tomography (EIT) has the potential to solve the issue by visualizing human respiratory system; however, its spatial resolution is too low to locate the real pulmonary lesions. One feasible method is to incorporate priori information into the process of EIT imaging. This study aims to investigate the conductivity characteristics of human lung tissues as priori information. First, the impedance spectra of human lung tissues in surgical operation were measured in time in a measurement system. Then, two‐ and three‐dimensional lung models were established. Finally, the conductivity distribution models of cancerous and normal lung tissues were established. Overall, the conductivity of left lung was larger than that of right lung; the conductivity of lower lobe was larger in left lung, whereas the conductivity of upper lobe was larger in right lung. When tissues became cancerous, the conductivity increased by 33.5% on average, and by 34.8 and 31.4% in the left and right lung, respectively. The conductivity in left lung changed more obviously; and it changed more obviously in posterior basal segment of lower lobe in left lung, as well as at apex in right lung. These conclusions can lay the foundation for the further research of improving the spatial resolution of EIT for lung cancer monitoring.