Description – Magnetocardiography (MCG) is a functional imaging system that is used for medical heart diagnosis. The so-called heart current induces a magnetic field that can be measured by an MCG system.This current results from the excitation of the heart muscle cells. Thus, the signal of the magnetic field exactly maps the electrophysiological activity of the heart. Due to this passive measuring technique, an MCG does not need any electromagnetic radiation. In comparison to an electrocardiography system (ECG), the heart signal is not evaluated by different ECG lead measurements. The MCG signal is acquired in the proximity of the heart. The signal can be detected almost without inhomogeneous influences or other restrictions. In particular, the MCG is more sensitive to tangential current as an ECG. Furthermore, an MCG is additionally sensitive to the vortex current (e.g. perpendicular to the tangent space) that cannot be detected by an ECG system. The MCG reconstruction task is based on the Biot-Savart law and leads to an inverse problem. Hence, its solution is also known as pseudo current. Biomagnetik Park GmbH (bmp) provides a high data quality with its MCG technology.The Superconducting Quantum Interference Devices (SQUIDs) can detect the MCG signal in the femto Tesla range. The medical sensitivity can be achieved currently up to approximately 97% (Park et al.1 Dobutamine stress magnetocardiography for the detection of significant coronary artery stenoses – A prospective study in comparison with simultaneous 12-lead electrocardiography).The article (Tao et al. Magnetocardiography based Ischemic Heart Disease Detection and Localization using Maschine Learning Methods) demonstrates that the potential of magnetocardiography is not exhausted analytically. Since the early detection of ischemic heart diseases based on MCG imaging is already ensured, e.g. refer to (Park and Jung Qualitative and quantitative description of myocardial ischemia by means of magnetocardiography), the functionality shall be now extended to a localization feature. i.e. it should be focused, where the stenosis is placed. The lack of morphological information in the signal results in an inverse problem that complexity increases proportionally to the accuracy of the model
Thesis objectives – To analyze and evaluate state-of-the-art deep learning methods, e.g. shown in (Maier et al. A gentle introduction to deep learning in medical image processing), in order to enable the MCG based localization of ischemic heart disease. Therefore, the publications Tao et al. should be used as a conceptual guideline. The objective of the master thesis consists in reconstructing at least the results of this article. In particular, the results shall be enhanced by the application of deep learning optimization methods and the sensitive bmp MCG data.