ObjectiveTo investigate the impact of electrical impedance tomography (EIT) as a means of assessing and guiding pulmonary rehabilitation chest physiotherapy (CPT) in hospital-acquired pneumonia (HAP) patients on the time to symptom improvement, cost of hospitalisation, length of stay and patient satisfaction with pulmonary rehabilitation. MethodsNinety-six patients with HAP requiring pulmonary rehabilitation were included in the study and were divided into control group and experimental group using the random number table method. Patients in both groups underwent pulmonary rehabilitation CPT in addition to clinical treatment for HAP, twice daily for 20 minutes each time. EIT was added to experimental group as a means of assessment and guidance, with dynamic review prior to treatment and real-time adjustment of treatment based on the results. The primary outcome indicator was a comparison of the change in clinical pulmonary infection score (CPIS) after the start of treatment in both groups, and secondary outcome indicators were a comparison of the length of HAP hospitalisation, HAP-related cost, and patient satisfaction with pulmonary rehabilitation in both groups. ResultsDuring the study, 8 patients terminated airway clearance due to the aggravation of the disease, and 1 patient referral. Finally, 43 patients in the study control group and 44 patients in the experimental group were included. There was a significant difference in CPIS between the test group and the control group on 3rd and 7th day after starting airway clearance (P<0.05). Compared with the control group, there were significant differences in the length of HAP hospitalisation, HAP-related cost, and patient satisfaction with airway clearance in the test group (P<0.01). ConclusionThe use of EIT allows real-time visual monitoring of the distribution of lung ventilation in HAP patients, thus guiding individualized pulmonary rehabilitation treatment, which can shorten the time to symptom improvement, reduce the length and cost of HAP hospitalisation and significantly improve patient satisfaction and compliance with pulmonary rehabilitation.
Electrical impedance tomography (EIT) is an emerging technology for real-time monitoring based on the impedance differences of different tissues and organs in the human body. It has been initially applied in clinical research as well as disease diagnosis and treatment. Lung perfusion refers to the blood flow perfusion function of lung tissue, and the occurrence and development of many diseases are closely related to lung perfusion. Therefore, real-time monitoring of lung perfusion is particularly important. The application and development of EIT further promote the monitoring of lung perfusion, and related research has made great progress. This article reviews the principles of EIT imaging, lung perfusion imaging methods, and their clinical applications in recent years, with the aim of providing assistance to clinical and scientific researchers.
Electrical impedance tomography (EIT) plays a crucial role in the monitoring of pulmonary ventilation and regional pulmonary function test. However, the inherent ill-posed nature of EIT algorithms results in significant deviations in the reconstructed conductivity obtained from voltage data contaminated with noise, making it challenging to obtain accurate distribution images of conductivity change as well as clear boundary contours. In order to enhance the image quality of EIT in lung ventilation monitoring, a novel approach integrating the EIT with deep learning algorithm was proposed. Firstly, an optimized operator was introduced to enhance the Kalman filter algorithm, and Tikhonov regularization was incorporated into the state-space expression of the algorithm to obtain the initial lung image reconstructed. Following that, the imaging outcomes were fed into a generative adversarial network model in order to reconstruct accurate lung contours. The simulation experiment results indicate that the proposed method produces pulmonary images with clear boundaries, demonstrating increased robustness against noise interference. This methodology effectively achieves a satisfactory level of visualization and holds potential significance as a reference for the diagnostic purposes of imaging modalities such as computed tomography.
Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.