Exploring Respiratory Motion Tracking through Electrical Impedance Tomography (EIT)

Qi Wang, Xiuyan Li, Xiaojie Duan, Ronghua Zhang, Hong Zhang, Yanhe Ma, Huaxiang Wang, Jiabin Jia

Research output: Contribution to journalArticlepeer-review

Abstract

Motion tracking is an effective approach for the management of respiratory motion during the medical imaging process, which has always been a major concern in diagnostic imaging, interventional, and non-invasive therapy. However, the low imaging speed of traditional medical imaging techniques limits the practical application of real-time motion tracking. Electrical impedance tomography (EIT) is proved to be an effective tool for continuous monitoring of lung activity/status. However, the respiratory motion has never been studied in the medical EIT field before. In this paper, preliminary research of lung movement during the respiratory process is first studied based on EIT. Multi-ring electrode thorax models under different respiratory statuses were constructed to obtain simulation data of EIT. A modified TV algorithm is used for the estimation of lung volume and movement based on 3D EIT images, which improve the quality of reconstruction by approximately 30% and 20% compared with the traditional Tikhonov method and the total variation (TV) method, respectively. Both simulations and experiments were conducted to show the potential of respiratory motion tracking through 3D EIT reconstruction.
Original languageEnglish
Article number4504712
JournalIEEE Transactions on Instrumentation and Measurement
Volume70
Early online date28 May 2021
DOIs
Publication statusE-pub ahead of print - 28 May 2021

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