Electrical impedance tomography for real-time 3D tissue culture monitoring

Electrical impedance tomography (EIT) is an emerging image technique that can image the spatial conductivity distribution in the sensing area by generating an electric field and measuring the induced boundary voltages. With the advantages of low-cost, high-temporal resolution, non-destructive and non-radiative, EIT has been developed for the clinical applications, including thorax imaging, lung ventilation monitoring, breast cancer screening and functional brain imaging. Its feasibility for monitoring the motion and conductivity change of the human tissues has been well investigated. It, therefore, shows enormous potential in the in-vitro cellular characterisation, where samples have the same electrical properties as in-vivo human tissues. Since conventional biological imaging techniques are mainly optimised for the monolayer cell culture, their performance is limited when processing the dense, highly scattering tissues, which can better mimic the in vivo situation than 2D cultured cells. Utilising EIT as a novel method to monitor these 3D samples may help to overcome the difficulties and improve the temporal resolution of the data. This thesis aims to evaluate the feasibility of miniature EIT for 3D sample imaging and improve its performance for real-time 3D tissue culture monitoring. Phantom studies were first carried out to evaluate the challenges of EIT imaging when performing in the sensors in the millimetre scale. Different imaging settings, including imaging modality and measuring frequency, were compared, and a combined regularisation method is proposed to improve the image quality. Besides, a physical model for 3D biological tissue was developed to estimate its equivalent conductivity through the electrical properties and volume fraction of cells. The spatial resolution of EIT for tissue culture imaging was examined based on the model. In addition, the protocols of time-difference and frequency-difference EIT for 3D tissue culture monitoring in tightly packed spheroids and sparsely distributed bioscaffolds have been developed and verified through the experiments utilising MCF-7 breast cancer cells. Moreover, equivalent circuit models were developed for the EIT measurement, and a joint simulation method combining the finite element model and equivalent circuit analysis was developed to analyse the measurement error in frequency-difference EIT. Finally, a calibration method was developed to eliminate the circuitry errors in frequency-difference EIT so that it can be applied for the long-term monitoring in biological applications. In summary, this thesis presents the research works on improving the robustness of miniature EIT to the measuring noise and the background disturbance through the optimization of experimental protocols, measuring methods and imaging settings. It shows the potential to be applied in biological research using 3D cell culture, including drug discovery and tissue engineering

Internet of Things for beyond-the-laboratory prosthetics research

Research on upper-limb prostheses is typically laboratory-based. Evidence indicates that research has not yet led to prostheses that meet user needs. Inefficient communication loops between users, clinicians and manufacturers limit the amount of quantitative and qualitative data that researchers can use in refining their innovations. This paper offers a first demonstration of an alternative paradigm by which remote, beyond-the-laboratory prosthesis research according to user needs is feasible. Specifically, the proposed Internet of Things setting allows remote data collection, real-time visualization and prosthesis reprogramming through Wi-Fi and a commercial cloud portal. Via a dashboard, the user can adjust the configuration of the device and append contextual information to the prosthetic data. We evaluated this demonstrator in real-time experiments with three able-bodied participants. Results promise the potential of contextual data collection and system update through the internet, which may provide real-life data for algorithm training and reduce the complexity of send-home trials. This article is part of the theme issue ‘Advanced neurotechnologies: translating innovation for health and well-being’

Arduino-based myoelectric control: Towards longitudinal study of prosthesis use

Understanding how upper-limb prostheses are used in daily life helps to improve the design and robustness of prosthesis control algorithms and prosthetic components. However, only a very small fraction of published research includes prosthesis use in community settings. The cost, limited battery life, and poor generalisation may be the main reasons limiting the implementation of home-based applications. In this work, we introduce the design of a cost-effective Arduino-based myoelectric control system with wearable electromyogram (EMG) sensors. The design considerations focused on home studies, so the robustness, user-friendly control adjustments, and user supports were the main concerns. Three control algorithms, namely, direct control, abstract control, and linear discriminant analysis (LDA) classification, were implemented in the system. In this paper, we will share our design principles and report the robustness of the system in continuous operation in the laboratory. In addition, we will show a first real-time implementation of the abstract decoder for prosthesis control with an able-bodied participant

Exploring the Potential of Electrical Impedance Tomography for Tissue Engineering Applications

In tissue engineering, cells are generally cultured in biomaterials to generate three-dimensional artificial tissues to repair or replace damaged parts and re-establish normal functions of the body. Characterizing cell growth and viability in these bioscaffolds is challenging, and is currently achieved by destructive end-point biological assays. In this study, we explore the potential to use electrical impedance tomography (EIT) as a label-free and non-destructive technology to assess cell growth and viability. The key challenge in the tissue engineering application is to detect the small change of conductivity associated with sparse cell distributions in regards to the size of the hosting scaffold, i.e., low volume fraction, until they assemble into a larger tissue-like structure. We show proof-of-principle data, measure cells within both a hydrogel and a microporous scaffold with an ad-hoc EIT equipment, and introduce the frequency difference technique to improve the reconstruction