Modification of EIT algorithms using a pipeline multiprocessor algorithm

Electrical impedance tomography (EIT) is relatively new medical imaging modality that produces images by computing electrical properties within the human body. In EIT, sinusoidal electric currents are applied to the body using electrodes attached to the skin, and voltage measurements that are developed on the electrodes are used. Using these data, a reconstruction algorithm computes the conductivity and permittivity distribution within the body. Several algorithms for EIT reconstruction that is able to track fast changes from incomplete data set in the impedance distribution are proposed. A modification to the EIT imaging systems proposed in the Kalman filter approach is presented. In the Kalman filter approach speedup is achieved by reduction of the parameter space. This paper presents modifications to the EIT imaging systems that allow continuously display conductivity, permittivity or magnitude of admittivity distributions in a subject for indefinite time intervals without any reduction of data. To achieve higher frame rates a pipeline multiprocessor algorithm (PMA) is used to solve an EIT inverse problem. The real-time imaging system proposed by Edic et al. (1995), and the Kalman filter approach proposed by Vauhkonen et al. (see IEEE Trans. on Biomedical Engineering, vol.45, no.4, p.486-93, 1998), are compared with proposed PMA. With proposed improvement we can obtain 2.8 times faster reconstruction than the Kalman filter approach, which means that the global reconstruction rate is approximately 90 times faster than with the conventional methods.