An Improved Tikhonov Regularization Method for Lung Cancer Monitoring Using Electrical Impedance Tomography

Abstract

Bedside monitoring plays an important role in the treatment of lung cancer. As a mostly used technique, X-ray computed tomography cannot provide medical surveillance for patients suffering from lung cancer in real-time. In comparison, the technique of electrical impedance tomography (EIT) has the potential to solve the issue by visualizing the human respiratory system; however, in most cases, its spatial resolution is too low to locate the real pulmonary lesions. One feasible method to improve the image quality is to incorporate the a priori information into the process of EIT imaging. In this paper, the conductivity distributions of lung tissues from a group of patients were extracted as the a priori information at first. Then, a novel EIT imaging method was proposed for lung cancer monitoring. A series of experiments indicated that the proposed method had potential capacity to identify lung cancer and monitor its metastasis. Besides, EIT can be further developed as a short- or long-term medical monitoring tool based on the proposed method.