In order to explore the feasibility of applying magnetic detection electrical impedance tomography (MDEIT) on respiratory monitoring, aiming at the forward problem of magnetic detection electrical impedance tomography, we calculated the electric potential and current density distribution inside the imaging object by using the finite element method. We then got magnetic induction intensity outside the object at the end of exhaling and inhaling according to Biot-Savart's law. The results showed that the magnetic induction intensity at the end of inhaling was 8.875%, less than that at the end of exhaling. By the simulation results, we could understand the difference of magnetic induction intensity value surrounding the lung at the end of exhaling and inhaling due to the change of lung volume and electrical conductivity distribution better. Our research laid the foundation for the late image reconstruction and clinical disease detection.
Cardiac three-dimensional electrophysiological labeling technology is the prerequisite and foundation of atrial fibrillation (AF) ablation surgery, and invasive labeling is the current clinical method, but there are many shortcomings such as large trauma, long procedure duration, and low success rate. In recent years, because of its non-invasive and convenient characteristics, ex vivo labeling has become a new direction for the development of electrophysiological labeling technology. With the rapid development of computer hardware and software as well as the accumulation of clinical database, the application of deep learning technology in electrocardiogram (ECG) data is becoming more extensive and has made great progress, which provides new ideas for the research of ex vivo cardiac mapping and intelligent labeling of AF substrates. This paper reviewed the research progress in the fields of ECG forward problem, ECG inverse problem, and the application of deep learning in AF labeling, discussed the problems of ex vivo intelligent labeling of AF substrates and the possible approaches to solve them, prospected the challenges and future directions for ex vivo cardiac electrophysiology labeling.