Current medical imaging uses MRI or CT images to diagnose tissue injuries. In addition to this classic procedure, there are also alternative technologies that have advantages against MRI or CT. These include electrical impedance tomography (EIT). With the help of EIT it is possible to obtain an initial screening of the body quickly and without a lot of hardware. Classical software-based methods of imaging reconstruction use a linear back projection or iterative approaches, such as Gauss-Newton algorithm. This paper introduces innovative approaches of artificial intelligence (AI) for imaging. For this purpose, extensive AI-based simulations with a Generative Adversial Network (GAN) are performed and the approaches are transferred to a gelatine phantom and to the human body within a small study.
Electrical Impedance Tomography (EIT) is a method used to record the impedance distribution within a target. The best-known application of EIT is lung diagnostics using imaging algorithms. However, apart from this, there are individual research projects dealing with imaging analysis at the cellular level. For example, cell analysis using EIT can help to distinguish diseased cells from healthy cells. To achieve this goal, an existing EIT system was combined with a new EIT chip in a first step. This chip allows analysis in very small dimensions. Various parameters such as the diameter of the measuring environment, the necessary conductive solutions or the measuring methods used were varied and evaluated. In a next step, various image reconstructions were carried out using data acquired with C. elegans.
KEYWORDS: Electrodes, Imaging systems, Tissues, Reconstruction algorithms, Multiplexers, Magnetic resonance imaging, Lung, Resistance, Analog electronics, Medical imaging, Mobile device imaging
Medical imaging is an integral part of today's world. Many diseases can be diagnosed with an examination of the inner structure of the body. Electrical impedance tomography (EIT) is an imaging method which is used in the medical field. In addition to the very widespread use of lung diagnostics, the EIT also finds application for cancer care in the female breast area. A non-existent application is the image reconstruction of the human extremities, especially arms and legs. In the future a fast imaging using a mobile EIT system can help to create a first diagnosis according to diseased tissue. Thus it is possible to decide on the spot whether a further treatment in a hospital is necessary. However, there is as yet no mobile EIT system that allows such a diagnosis on site. Current EIT systems are not suitable for mobile application which is a major obstacle for exploring the techniques capabilities. For this application, an EIT system has been designed that is mobile and allows fast image analysis. It is powered by a rechargeable battery and offers a wireless interface to connect to a host. The practical evaluation is carried out with the determination of the measuring functionality on a phantom. In the next step first measurements are presented on the human body.
Radioactive isotopes with energies up to 0.5 MeV are used in nuclear medicine for imaging. However several isotopes with energies up to 10 MeV exist that have interesting properties for medical applications, but conventional detectors are inefficient for these energies. A Compton camera setup, consisting of a radiator and an absorption layer, can be used to detect such high energy gamma radiation. In a Compton camera an incident gamma ray undergoes a Compton scattering in the radiator creating a high energetic Compton electron e. By determining the point of interaction and measuring the energy and the direction of the scattered gamma ray it is possible to confine the origin of the incident gamma ray to the surface of a cone. The greatest challenge lies in the coincident detection of electron and scattered gamma. Previous research proposed the use of Silicon Photomultipliers arrays (SiPM) to detect Cherenkov Light (CL) produced by e for determining es properties based on the directional properties of CL. Since only few photons of CL are produced, the high noise floor of the SiPM affects the detection negatively. In this contribution an estimation of SiPMs noise floor is presented, that bases on a behavioural simulation of noise processes in the SiPM. With the simulation it is possible to determine properties of the SiPM, to assess the effectiveness of filter and to build stimuli for other simulations.
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