Yanbin Xu

Electrical impedance tomography (EIT) is a non-invasive, radiation-free and real-time functional imaging technique with significant potential for pulmonary monitoring. While three-dimensional (3D) reconstruction offers comprehensive impedance information of the lungs over two-dimensional (2D) reconstruction, it faces challenges from increased unknown conductivity parameters, domain expansion and severe ill-posedness of the inverse problem. To address these, we propose Physics-Guided Diffusion Reconstruction (PGDR), a novel framework integrating generative diffusion models with physical constraint. PGDR leverages diffusion models to learn 3D conductivity priors and incorporates voltage measurements into the sampling process for consistency with both voltage measurements and predictions. We employ score-based diffusion models (SDM) and denoising diffusion implicit models (DDIM) with PGDR, comparing against Graz consensus (GREIT), Tikhonov and U²-Net algorithms via simulations and phantom experiments. In simulation experiments, the Physically Guided DDIM Reconstruction (PGDDIR) method demonstrates superior performance, achieving peak signal-to-noise ratio (PSNR) gains of 18.949 dB, 29.978 dB, and 34.397 dB over U²-Net, Tikhonov, and GREIT, respectively, and structural similarity index (SSIM) improvements of 0.138, 0.059, and 0.463 compared to these methods. Its pixel accuracy (PA) and intersection over union (IoU) rank second. In tissue-mimicking phantom experiments, PGDDIR attains the highest PA and IoU values, with PSNR and SSIM ranking second. These findings underscore diffusion-based frameworks’ promise for accurate, robust 3D EIT lung imaging.

Electrical impedance tomography (EIT) is a new non-invasive functional imaging technology, which has the advantages of non-invasion, non-radiation, low cost, fast response, portability and visualization. In recent years, more and more studies have shown that EIT has great potential in the detection of lung diseases and has been applied to early diagnosis and treatment of some diseases. This paper introduced the basic principle of EIT, discussed the research and clinical application of EIT in the detection of acute respiratory distress syndrome, chronic obstructive pulmonary disease, pneumothorax and pulmonary embolism, and focused on the summary and introduction of indicators and functional images of EIT related to the detection of lung diseases. This review will help medical workers understand and use EIT, and promote the further development of EIT in lung diseases as well as other fields.

Crush injuries are an unfortunately common occurrence during natural disasters such as earthquakes. Apart from direct injuries to vital organs such as the brain and heart, crush injuries resulting from compression of the body by collapsed infrastructure are a major cause of fatalities. Therefore, early diagnosis and prompt treatment of crush injury at the disaster scene can reduce mortality. Electrical Impedance Tomography (EIT) is a functional imaging technique that reconstructs internal conductivity distribution of a body based on external electrical measurements. Applying different excitation-measurement patterns results in differences in sensitivity and spatial resolution. This paper designs and fabricates a flexible planar sensor array featuring a rectangular electrode configuration, accompanied by the proposal of two excitation-measurement patterns. The efficacy of these patterns was evaluated through numerical simulations in terms of sensitivity index and image reconstruction quality. Further assessment of image reconstruction quality was conducted using agar-based tissue-mimicking phantoms as biomimetic physical models. Moreover, the flexible array employing the optimal excitation-measurement pattern was experimentally applied to detect crush-induced electrical property changes in Sprague-Dawley (SD) rats, thereby preliminarily validating its feasibility for in vivo applications.

Automatic and accurate segmentation of the optic disc (OD) region has practical applications in the medical field. In this study, a novel encoder-decoder network is proposed to segment the ODs automatically and accurately. The encoder consists of three parts: (1) A low-level feature extraction module composed of dense connectivity block (Dense Block) which can output rich low-level features; (2) A High-resolution Block (HR Block) which can extract sufficient semantic information while reducing parameters; (3) An Atrous Spatial Pyramid Pooling (ASPP) module is used to obtain high-level features. Therefore, the network is named DHANet. The proposed decoder takes advantage of the multi-scale features from the encoder to predict OD regions. By comparing it with existing classic models such as U-Net, CE-Net, and DeepLabv3+,as well as the latest excellent U-Net++, Attention U-Net and CrackSegNet, it has been proven that the proposed method can generally achieve better segmentation performance at a lower cost. The ablation studies proved the influence of each module on the segmentation performance, and explained the network structure reasonably. In the case of fewer network parameters, DHANet achieves better prediction performance on intersection over union (IoU), dice similarity coefficient (DSC) and other evaluation metrics. DHANet is lightweight relatively and can use multi-scale features to predict OD regions.

Pressure ulcers are a particularly high incidence of chronic trauma. When a superficial wound is visible, the underlying wound is often serious, so early detection of pressure ulcers is critical. Currently there is still no technique or real time monitoring system that can visualize progressive tissue damage of pressure ulcers. Electrical impedance tomography (EIT) is a functional imaging technique that can aid in the diagnose of tissue health by visualizing the distribution of bioelectrical impedance parameters. Based on the differences between the electrical properties of pressure ulcers and normal tissue, a non-invasive pressure ulcer depth detection method based on EIT is proposed. Given surface voltage data measured on an open rectangular electrode array, the conductivity distribution under the skin surface was reconstructed to obtain pressure ulcer depth information which is expected to visualize progressive tissue damage. Based on EIT combined with flexible sensor arrays, finite element simulation models and tissue-mimicking agar phantom models were established to verify the effectiveness of the EIT-based depth detection method of pressure ulcers. The effects of time-difference imaging and frequency-difference imaging on the depth detection of pressure ulcer were compared. Both simulation and physical phantom experiments demonstrated the feasibility of EIT for detection of early pressure ulcer depth.

To improve the resilience of the power system against typhoon events, a two-stage robust optimization (TRO) model considering the line failure stochasticity is proposed. The generator status and output are pre-scheduled in the first stage to minimize operation costs. In the second stage, a recourse method is applied to identify the worst-case failure scenario of the transmission line within a polyhedral uncertainty set. This uncertainty set considers line repair and non-repair strategies, and the impact of typhoons on transmission lines is quantified as the failure probability. The objective function of the second stage is to minimize the load shedding and generator curtailment costs of power system due to circuit breaker (CB) operation. The column-and-constraint generation (C&CG) algorithm is used to solve this TRO model. The proposed model is tested on the IEEE 24-reliability test system (RTS) under a typhoon to verify its effectiveness.

Pressure ulcers are chronic skin wounds with a particularly high morbidity and a relatively low recovery rate. At present, there is still a lack of a portable and visual real-time monitoring system for early pressure ulcers. Electrical impedance tomography (EIT) is a kind of functional imaging technique that can diagnose the healthy condition in the measured tissues by visualizing the distribution of bioelectrical impedance parameters. According to the principle of electrical characteristic of pressure ulcers, i.e., changes in physiological and pathological information of tissues caused by pressure lead to changes in electrical impedance of tissues, a non-invasive imaging system of pressure ulcers based on EIT with novel flexible electrode array is proposed. Two kinds of 3D finite element simulation models are constructed to discuss the measurement effects of the flexible array on simulated pressure ulcers at different locations, depths, and with different sizes. The effects of different imaging algorithms on imaging results are also compared and analyzed. Physical phantom model experiments and preliminary experiments on human skin are conducted. Both simulations and physical experiments indicate that EIT technique is excepted to realize a safer, portable, low-cost and visual real-time monitoring system for early pressure ulcers.

Focused ultrasound (FUS) has been studied for different medical applications according to tissue responses to FUS with different intensities. At present, studies on responses to low-intensity focused ultrasound (LIFU) are mainly focused on mechanical responses and that to high-intensity focused ultrasound (HIFU) are focused on thermal responses. However, studies have shown that electrical responses can be produced by LIFU. In addition, modeling the complex energy conversion process of FUS from low to high intensity is challenging due to the computational complexity caused by multiphysics coupling and finite-element calculation. Based on the physical mechanism of each response to FUS with different intensities, a complete finite-element simulation platform is established to allow researchers to simulate not only the mechanical responses to LIFU and thermal responses to HIFU but also the electrical responses to LIFU. A multiphysics coupling method, which combines pressure acoustics field, solid mechanics field, electric currents field, and bioheat transfer field together, is used to enhance the matching among different physical fields and further reduce time consuming and improve simulation accuracy. The influences of ultrasound parameters and tissue properties on responses to FUS are discussed. Furthermore, experiments are implemented to validate this simulation platform. The simulation results are in good agreement with the theoretical derivation and experimental results as well as results reported previously, verifying the rationality of the simulation platform. This complete simulation platform is powerful for utilizing FUS with different intensities and can be further used for ultrasound elastography, acoustoelectric imaging, HIFU, and other related techniques.

Multifrequency electrical impedance tomography (MFEIT) has been rapidly developed and has shown great potential for biomedical analysis and imaging. This article presents the design and evaluation of a wideband and high-precision MFEIT system which incorporates novel features to fit biomedical application. First, a high-accuracy multifrequency current source with adjustable output frequency and amplitude as well as high-output impedance is designed based on the combination of the classic current mirror and a differential topology, which demonstrates stable performance in high frequencies. Second, a reference measurement circuit is developed for the calibration of phase measurement data through which precise measurements of both amplitude data and phase data can be obtained. Furthermore, the low-pass filters utilized in the system have been designed based on the Butterworth filter theory to realize the flattest frequency response in the passband. The MFEIT system is systematically tested and evaluated in terms of filter performance, signal to noise ratio (SNR), channel consistency and measurement repeatability, flatness analysis and load dependence as well as imaging experiments. Experimental results showed that the maximum amplitude SNR and phase SNR of the boundary measurements are 71.55 and 64.12 dB, respectively. The evaluation results indicate that the system has good performance over a wide frequency band (100 Hz–1 MHz) and achieves a relatively high SNR.