Motion tracking is an effective approach for the management of respiratory motion during the medical imaging process, which has always been a major concern in diagnostic imaging, interventional, and noninvasive therapy. However, the low imaging speed of traditional medical imaging techniques limits the practical application of real-time motion tracking. Electrical impedance tomography (EIT) is proved to be an effective tool for continuous monitoring of lung activity/status. However, the respiratory motion has never been studied in the medical EIT field before. In this article, preliminary research of lung movement during the respiratory process is first studied based on EIT. Multiring electrode thorax models under different respiratory statuses were constructed to obtain simulation data of EIT. A modified TV algorithm is used for the estimation of lung volume and movement based on 3-D EIT images, which improve the quality of reconstruction by approximately 30% and 20% compared with the traditional Tikhonov method and the total variation (TV) method, respectively. Both simulations and experiments were conducted to show the potential of respiratory motion tracking through 3-D EIT reconstruction.

As a new generation of noninvasiveness imaging modality, electrical impedance tomography (EIT) has broad application prospects to perform functional evaluation for human tissues and organs. Deep learning makes great breakthrough in recent years, and many different neural networks (NNs) have been applied to EIT image reconstruction for obtaining high resolution reconstruction images. However, the NN is hard to interpret, and the robustness and generalization ability of the network model cannot be guaranteed. In this study, a deep learning scheme namely error-constraint network (Ec-Net) is designed to remove the error of reconstruction images obtained by traditional reconstruction algorithm. By establishing the residual mapping between image and error, Ec-Net achieves more robust reconstruction performance and faster learning. Feature fusion module, dilated convolution block, and the structure of residual in residual are used to further improve the reconstruction accuracy for the inclusion boundary. The reconstruction results show that the proposed method can accurately reconstruct the irregular and sharp boundary of inclusions while showing good anti-noise ability for measurement noise higher than 30 dB. Even effective reconstruction can be obtained for new inclusions that have size/shape variations, which proves the generalization ability of Ec-Net model.

The image reconstruction of electrical impedance tomography (EIT) is highly ill-posed and nonlinear. Because of the poor nonlinear fitting ability of analytical algorithms, reconstructed images of these algorithms are blurry and lack detailed features. Although high-quality EIT images can be obtained by applying deep learning networks to image reconstruction, the interpretability and generalization ability of the network are difficult to guarantee. A deep learning structure namely conditional wasserstein generative adversarial network with attention mechanism (CWGAN-AM) is proposed for EIT image reconstruction. CWGAN-AM consists of imaging module, generator and discriminator. The initial conductivity image obtained by the imaging module is added to both the generator and discriminator as a constraint to improve the stability of reconstruction. The enhanced residual blocks (ERBs), structure of residual in residual (RIR) and attention unit are used in the generator to further improve the reconstruction accuracy for the inclusion boundary. The imaging results indicate that CWGAN-AM can accurately recover the irregular boundaries of inclusions, and effective reconstruction can be accomplished for the new conductivity distribution (inclusions with size/shape variations) and noisy samples.