Electrical resistance tomography (ERT) and electrical impedance tomography (EIT) are imaging techniques reconstructing the internal conductivity distribution image of an object based on voltage measurements at the periphery of the object with a given applied current. ERT uses a direct current (DC), while EIT uses an alternating current (AC). However, for low frequencies both ERT and EIT have the same governing equation, which is often referred to as a non-linear and ill-posed inverse problem. Both methods have diverse applications in biology, biomedicine, and industry. This master’s degree project aims to create a low-cost imaging system for the ERT, which is the main focus, as well as for the EIT. The project includes three main components: 1) Simulations and reconstructions using EIDORS (Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software), 2) Developing an experimental workbench (a measurement system), and 3) developing a machine learning model for the ERT. EIDORS was used to simulate and reconstruct ERT and EIT images. It was also used to generate training data for the machine learning model to be developed. The measurement system includes a circular water tank with electrodes, power supplies, and measurement units. Tanks with 8 and 16 electrodes were designed using 3D printers. Initially, aluminium electrodes provided inconsistent measurements due to magnetization and electrolysis, later replaced by graphite electrodes, offering better but not yet accurate enough results. After implementing reconstruction algorithms in EIDORS, a machine learning model was developed for ERT. It involved: 1) generating a training set, containing over 5000 simulated data points, 2) preprocessing the generated data set which included PCA dimensionality reduction, 3) and lastly a linear regression model developed. The model struggled with small object detection and occasional inconclusive results, likely due to limited training dataset diversity. Additionally, images of two cases were reconstructed using EIT and comparing it to ERT it can be concluded that EIT performs better than ERT.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-514389 |
| Date | January 2023 |
| Creators | Aso Abbas, Ismail, Isaksson Sandberg, Mats |
| Publisher | Uppsala universitet, Signaler och system |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC F, 1401-5757 ; 23062, UPTEC E, 1654-7616 ; 23018 |
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