A Data Compensation Method for Reducing Impact of Scalp Dehydration in Electrical Impedance Tomography

Electrical impedance tomography (EIT) has shown its potential in the monitoring of cerebral edema. It is found that scalp is also dehydrated when dehydration of brain tissue is conducted. Simultaneous dehydration of the brain tissue and scalp would greatly affect the image quality. To solve this problem, this work proposes a data compensation method which aims to reduce the influence of scalp dehydration on image reconstruction. Firstly, a rabbit head which is simulated by a three-layer elliptical model is established. The relationship between boundary measurement and dehydration degree in the case of simultaneous dehydration is investigated. Then a prior matrix representing variation of boundary voltage against degree of dehydration when only scalp is dehydrated is established. With the determination of dehydration degree and establishment of the prior information, it is able to reduce the impact of scalp dehydration on measured voltage data. To validate the performance of the proposed method, reconstruction of four different models when scalp and brain are simultaneously dehydrated is conducted. Image reconstruction is performed under noiseless condition and different noise level conditions. Also, reconstructed images under brain dehydration are given for comparison. The performance of the proposed method is also experimentally tested. The results show that the impact of scalp dehydration on image reconstruction can be effectively reduced with the proposed data compensation method. • A novel data compensation method and a simple L1 regularization method is proposed for accurate image reconstruction in EIT. • By analyzing boundary data measured from electrodes, dehydration degree is determined and a prior matrix is obtained. • In the case of simultaneous dehydration of scalp and brain, reconstruction quality is improved with the proposed method.