An Approximate Point-Spread Function for Electrical Impedance Tomography Imaging

• Published in: IEEE access 2024, Vol.12, p.128246-128254
• Main Authors: Huynh, Hoang Nhut, Tuan Nguyen Diep, Quoc, Tran, Anh Tu, Tak Shing Ching, Congo, Nghia Tran, Trung
• Format: Article
• Language: English
• Subjects: Approximate computing; Biomedical imaging; Blurring; Computational modeling; Conductivity; Convolution; Data models; Deconvolution; Deep learning; Electric potential; Electrical impedance; Electrical impedance tomography; Electrical resistivity; Electrodes; Image reconstruction; Imaging; Inverse problems; Machine learning; Numerical methods; Point spread functions; point-spread function; Spatial resolution; Tomography; Voltage
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3456831

In Electrical Impedance Tomography, the ill-posed nature of the inverse problem and the interaction between the electric current and objects cause poor spatial resolution and blurring in the reconstructed images. This study proposes a numerical method, called the voltage-dependent point-spread function, which depends on the initial setting of the environment, such as voltage, conductivity, permeability, and frequency, to overcome the above problems. When the object layout is convoluted with a point spread function, the Electrical Impedance Tomography image can be reconstructed regardless of the conditions in forward and inverse problems, which is suitable for applications requiring big data, such as deep learning or machine learning. The object's size in the Electrical Impedance Tomography image can be reconstructed to approximately the real size by deconvolution with a point-spread function. The proposed method was validated using conventional methods through simulations and experimental experiments. The results demonstrated the feasibility and applicability of this method in clinical practice.