Biological electrical impedance tomography method

Abstract

The invention discloses a biological electrical impedance tomography method based on the particle swarm optimization and the regularization Gauss-Newton iteration algorithm. The method comprises during biological electrical impedance tomography problem solving, generating an initial value approximating to a true value as the initial value of the regularization Gauss-Newton iteration algorithm through the particle swarm optimization, then solving an inverse problem through the regularization Gauss-Newton iteration algorithm. The biological electrical impedance tomography method has the advantages that uneven subdivision is utilized during direct problem calculation to improve the tomography precision; the standard particle swarm optimization is utilized for generating one initial value for the regularization Gauss-Newton iteration algorithm, accordingly the defect that Newton type algorithms are sensitive to initial values can be well overcome, and meanwhile, only one approximate value approximating to the true value needs to be generated, so that low precision requirements can be achieved, further a relatively small number of particles and times of iterations are needed and the calculation amount can be reduced largely; image reconstruction is performed through the regularization Gauss-Newton iteration algorithm, so that high image precision and accurate image positioning can be achieved.

Description

technical field [0001] The invention provides a bioelectrical impedance imaging method, in particular to a bioelectrical impedance imaging method based on particle swarm and regularized Gauss-Newton iterative algorithm. Background technique [0002] Bioelectrical impedance imaging technology is the application of electrical impedance imaging technology (Electrical Impedance Tomography, EIT) to biomedical engineering. This technology can analyze the functional activities of biological tissues through image information, so as to obtain information closely related to the generation of biological tissue lesions. In "Principles of Electrical Impedance Imaging" (Science Press, 2009) by He Wei, Luo Ciyong, Xu Zheng, etc., EIT technology injects a safe current into the object through electrodes arranged on the surface of the object to be measured, and at the same time Measure the voltage values ​​on the remaining electrodes, and use the measured voltage information to obtain the el...

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Problems solved by technology

[0006] Differential evolutionary algorithm is a heuristic algorithm, it can minimize the nonlinear non-differentiable continuous space function, it can be classified as evolutionary optimization algorithm, its structure is simple, fast convergence, easy to apply, but its performance can not meet the medical imaging needs

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