As a non-invasive and radiation-free imaging modality, electrical impedance tomography (EIT) has attracted much attention in the last two decades and owns many industry and biomedical applications. However, due to the nonlinearity and ill-posedness of its inverse problem, the EIT images always suffer from low spatial resolution and are sensitive to the modelling errors. To achieve high resolution and modelling error robust EIT image, a two-stage deep learning (TSDL) method is proposed. The proposed method consists of a pre-reconstruction block and a convolutional neural network (CNN). The pre-reconstruction block learns the regularization pattern from the training data set and provides a rough reconstruction of the target. The CNN post-processes the pre-reconstruction result in a multi-level feature analysis strategy, and eliminates the modelling errors with prior information of the observation domain shape. The pre-reconstruction and CNN blocks are trained together by using a minimum square approach. To evaluate the performance of the TSDL method, the lung EIT problem was studied. The training data set is calculated from more than 100 thousand EIT simulation models generated from CT scans across 792 patients. Lung injury, measurement noise and model errors are randomly simulated during the model generation process. The trained TSDL model is evaluated with simulation testes, as well as the experimental tests from a laboratory setting. According to the results, the TSDL method could achieve high accuracy shape reconstructions and is robust against measurement noise and modelling errors.

A statistical shape constrained reconstruction (SSCR) framework is presented to incorporate the statistical prior information of human lung shapes for lung electrical impedance tomography (EIT). The prior information is extracted from 8000 chest computed tomography scans across 800 patients. The reconstruction framework is implemented with two approaches- a one-step SSCR and an iterative SSCR in lung imaging. The one-step SSCR provides fast and high accurate reconstructions of healthy lungs; whereas the iterative SSCR allows to simultaneously estimate the pre-injured lung and the injury lung part. The approaches are evaluated with simulated examples of thorax imaging and also with experimental data from a laboratory setting, with difference imaging considered in both approaches. It is demonstrated that the accuracy of lung shape reconstruction is significantly improved. In addition, the proposed approaches are proved to be robust against measurement noise, modeling error caused by inaccurately known domain boundary and the selection of the regularization parameters.

As a nondestructive, nonradioactive, and high temporal resolution technique, electrical impedance tomography (EIT) has potential applications in industrial and biomedical imaging. A novel geometrically constrained boundary reconstructor (GCBR) is presented for EIT. It can directly calculate the boundary of the target inclusions embedded into a homogeneous background conductivity and is robust to noise. The proposed GCBR depends on a specially designed energy function, which consists of the residual term and the geometric constraints' term. The residual term drives the estimated inclusion boundary to the targets. The geometric constraints' term regularizes the estimations and automatically excludes the meaningless boundary guesses from the candidate solution. A set of numerical tests are conducted to discuss the key factors influencing the boundary reconstruction performance. A set of experimental tests are conducted to evaluate the performance of the proposed GCBR. The position, size, and shape of the target inclusions are well reconstructed from the real data. The mean position error and mean Hausdorff distances between the target and reconstructed boundaries are 0.39% and 2.2% of the maximal observation diameter, respectively. The mean area errors and the mean area of the symmetric difference between the target and reconstructed inclusion regions are 0.32% and 2.54% of the total observation area, respectively.

A deep neural network is proposed for solving the dynamic image reconstruction problems in electrical impedance tomography (EIT), which can realize the filtering, smoothing, and prediction of the dynamic conductivity reconstruction. This framework includes a reconstruction network, convolutional neural network (CNN) encoder, recurrent neural network (RNN) model, and CNN decoder, thus is termed by RCRC. The RCRC can automatically learn prior spatial–temporal information from the voltage-to-conductivity training dataset and utilize it to enhance the conductivity reconstruction accuracy. Circular acceleration and pendulum systems are simulated with a water tank model. Stochastic data interpolation and dynamic data synthesis methods were proposed to generate large-scale dynamic dataset from a small-scale static dataset. The experimental results show that RCRC can accurately recover dynamic conductivity images from EIT noisy voltage sequence. Long-term conductivity prediction was also achieved by using the proposed network.

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.