Weak voltage signal enhancement for accurate image reconstruction in the craniocerebral EIT

As a promising imaging technique, electrical impedance tomography (EIT) is used to reflect the conductivity distribution variation of human tissues. Different from the lung EIT, the application of the craniocerebral EIT is challenging. This is attributed to the fact that the skull with high resistivity greatly restricts the injected current from flowing into the brain tissue. Consequently, the measured voltage signal is very weak causing poor reconstructed images. To solve this problem, a new strategy based on a multi-layer convolutional neural network (CNN) is proposed for weak voltage signal enhancement. Voltage measurements from the three-layer head model and the single-layer head model perform as the input and the output of the network respectively. The trained network is supposed to enhance the voltage data of the three-layer head model. To test the performance of the proposed method, voltage data processed by the multi-layer CNN is compared with the single-layer voltage data. Besides, comparisons are also made in the case of noise interruption and when the skull thickness varies. The results demonstrate that the processed voltage data is almost consistent with the single-layer voltage data. Compared with the image reconstruction with the three-layer voltage data, there is a large improvement when using the proposed method.