基于深度神经网络的电阻层析成像重建方法

电阻层析成像技术因其非侵入式测量特点、结果可视化的直观性和测量方法便捷性,被广泛用于医学造像,两相流工业检测和特殊材料检测。但其图像重建的逆过程由于固有的欠定性、病态特点,使得结果有一定偏差。针对该情况,设计了基于Resnet34改进的深度神经网络来求解电阻抗层析成像逆问题。通过设置场域内以像素点为中心,小范围内随机半径与电阻率分布变化强度,正向计算仿真32电极情况下各电极处边界电压,以此建立训练与测试数据集。经调参、训练后,该方法能较快收敛,并和高斯-牛顿法、全变差法以及Tikhonov正则化算法相比较,得到较好的判定性能。

Reconstruction method of electrical resistance tomography based on deep neural network

Electrical Impedance Tomography was widely used in medical imaging, two-phase flow industrial inspection and special material inspection due to its non-invasive measurement characteristics, intuitive results visualization and convenient measurement methods. However, the inverse process of image reconstruction is inherently under-determined and ill-conditioned, resulting in some deviations in the results. An improved deep neural network based on Resnet34 is designed to solve the inverse problem of electrical impedance tomography for it. The training and test data sets were established, by setting the pixel point as the center in the field, the random radius and resistivity distribution change intensity in a small range, and the boundary voltage at each electrode in the case of 32 electrodes was simulated forwardly. The method was proved to be able to converge quickly, and can obtain better judgment performance compared with Gauss-Newton iteration method, total variation method and Tikhonov regularization algorithm after parameter adjustment and training.