过程层析成像 (Process tomography)的逆问题也称为成像算法，它不仅需要反映被测物质与激励场的相互作用原理，而且应与传感器的空间阵列结构相匹配.成像算法的性能好坏 (包括图象质量和每帧计算需时 )是过程层析成像技术能否应用于工业过程监控系统的关键问题之一.为了得到性能良好的重建图象，提出了一种线性神经网络图象重建算法.该算法首先通过建立光学层析成像的正问题和逆问题的线性化模型来求解正问题，以得出图象和投影的关系模式对，然后将其用于训练和构造线性神经网络 ;最后使用训练好的线性神经网络来映射光学层析成像的逆问题.实验表明，该方法具有较高的图象质量和极高的成像实时性，是一种性能良好的图象重建算法

The inverse problem of process tomography is also named imaging algorithms, it not only agree with the interactional principle of the stimulating field and measured substance, also match the spatial array geometry of sensors. The performance of proposed imaging algorithms(including image quality and calculating interval per frame) is a key question whether the process tomography can be applied to the industry process monitoring and control system. In order to get excellent reconstructing image, a sort of Linear Neural Networks method for image reconstruction are proposed. Through building the linear models of forward and inverse problems to optical tomography, this algorithms calculate the forward problem firstly to obtain pairs of modes of image projections relation, then which are used to train and build the Linear Neural Networks; Finally, the inverse problem can be reflected through using the trained linear neural networks. The numerical simulation demonstrated that the method is a robust imaging algorithm, the quality of image is excellent, and the temporal performance of imaging is very good with this method.