A New Receiver Design for Biomedical Magnetic Induction Tomography with a Deep Learning Method

Abstract

Medical imaging is an essential aspect of modern medicine, providing a non-invasive technique for the diagnosis, monitoring and treatment of a wide range of conditions. However, there is a constant need for advancements to reduce radiation exposure, enhance patient comfort, and increase accessibility. As a cheap, contactless and non-hazardous imaging method, Magnetic Induction Tomography (MIT) could offer a new alternative to established imaging methods. First introduced in the early 1990s, MIT is still at the basic research stage and under constant change in the technical requirements. The focus of this study is a recently developed planar setting that offers several advantages, particularly in the image reconstruction of voluminous bodies in a biomedical setting. Due to the novelty of this structure, it is necessary to fundamentally reexamine both the forward and inverse problem of MIT. Here, a deep neural network is used to compare an established receiver setting with a new receiver design to improve reconstruction quality. Additionally, a testing method for new receiver setups is introduced.

Keywords

• magnetic induction tomography
• deep learning
• image reconstruction
• inverse problems