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一种用于解决逆介电常数问题的迭代牛顿-拉弗森方法。

An iterative Newton-Raphson method to solve the inverse admittivity problem.

作者信息

Edic P M, Isaacson D, Saulnier G J, Jain H, Newell J C

机构信息

General Electric Corporate Research and Development, Schenectady, NY 12309, USA.

出版信息

IEEE Trans Biomed Eng. 1998 Jul;45(7):899-908. doi: 10.1109/10.686798.

Abstract

By applying electrical currents to the exterior of a body using electrodes and measuring the voltages developed on these electrodes, it is possible to reconstruct the electrical properties inside the body. This technique is known as electrical impedance tomography. The problem is nonlinear and ill conditioned meaning that a large perturbation in the electrical properties far away from the electrodes produces a small voltage change on the boundary of the body. This paper describes an iterative reconstruction algorithm that yields approximate solutions of the inverse admittivity problem in two dimensions. By performing multiple iterations, errors in the conductivity and permittivity reconstructions that result from a linearized solution to the problem are decreased. A finite-element forward-solver, which predicts voltages on the boundary of the body given knowledge of the applied current on the boundary and the electrical properties within the body, is required at each step of the reconstruction algorithm. Reconstructions generated from numerical data are presented that demonstrate the capabilities of this algorithm.

摘要

通过使用电极向身体外部施加电流并测量这些电极上产生的电压,可以重建身体内部的电特性。这种技术被称为电阻抗断层成像。该问题是非线性且病态的,这意味着远离电极的电特性的大扰动在身体边界上只会产生小的电压变化。本文描述了一种迭代重建算法,该算法可得到二维逆导纳问题的近似解。通过进行多次迭代,由该问题的线性化解导致的电导率和电容率重建中的误差会减小。在重建算法的每一步都需要一个有限元正向求解器,它在已知身体边界上的施加电流和身体内部电特性的情况下预测身体边界上的电压。给出了从数值数据生成的重建结果,展示了该算法的能力。

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