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

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 thea prioriinformation into the process of EIT imaging. In this paper, the conductivity distributions of lung tissues from a group of patients were extracted as thea prioriinformation 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.