Abstract
Dynamic electrical impedance tomography (EIT) promises to be a valuable technique for monitoring the development of brain injury. But in practical long-term monitoring, noise and interferences may cause insufficient image quality. To help unveil intracranial conductivity changes, signal processing methods were introduced to improve EIT data quality and algorithms were optimized to be more robust. However, gains for EIT image reconstruction can be significantly increased if we combine the two techniques properly. The basic idea is to apply the priori information in algorithm to help de-noise EIT data and use signal processing to optimize algorithm. First, we process EIT data with principal component analysis (PCA) and reconstruct an initial CT-EIT image. Then, as the priori that changes in scalp and skull domains are unwanted, we eliminate their corresponding boundary voltages from data sets. After the two-step denoising process, we finally re-select a local optimal regularization parameter and accomplish the reconstruction. To evaluate performances of the signal processing-priori information based reconstruction (SPR) method, we conducted simulation and in-vivo experiments. The results showed SPR could improve brain EIT image quality and recover the intracranial perturbations from certain bad measurements, while for some measurement data the generic reconstruction method failed.
Highlights
Dynamic electrical impedance tomography (EIT) is an imaging technique mostly used to recover the conductivity distribution changes inside a body[1]
The red spheres represent perturbations and their conductivity values are set at 0.7 S/m, which is equivalent to the blood
To optimize the results of EIT in the application of brain injury monitoring, we proposed a novel reconstruction process signal processing-priori information based reconstruction (SPR)
Summary
Dynamic electrical impedance tomography (EIT) is an imaging technique mostly used to recover the conductivity distribution changes inside a body[1]. As the time scale of brain injury monitoring is roughly hours, it is difficult for us to avoid these factors and maintain the measurements stable all the time For this challenging problem, introducing effective signal processing methods or optimizing reconstruction algorithm with priori information are helpful. Signal processing methods can be used to refine data sets and we can select a better regularization parameter to improve image quality. Based on the above analysis, we made an optimization on brain EIT reconstruction process and proposed a signal processing-priori information based reconstruction (SPR) method. To investigate the performance of SPR in brain EIT reconstruction, we conducted simulation and human experiments, in which typical errors like Gaussian noise and bad electrode-skin contact were considered.
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