Neural network multi-criteria optimization image reconstruction technique (NN-MOIRT) for linear and non-linear process tomography

In this work, an analog neural network is utilized to develop a new image reconstruction technique for the linear as well as the non-linear process tomography. The ultrasonic computed tomography (CT) and the electrical capacitance tomography (ECT) are chosen to represent the linear and the non-linear tomography. The image reconstruction technique is based on a multi-criteria optimization, namely neural network multi-criteria optimization image reconstruction technique (NN-MOIRT). The optimization technique utilizes multi-objective functions: (a) the negative entropy function, (b) the function of the least weighted square error of projection (integral) values between the measured data and the estimated projection data from the reconstructed image, and (c) a smoothness function that gives a relatively small peakedness in the reconstructed image. The optimization image reconstruction problem is then solved using the Hopfield model with dynamic neural-network computing. The technique has been tested using simulated and measured data; this technique has shown significant improvement in accuracy and consistency compared with other available techniques for both linear and non-linear tomography.