Abstract
Tissue engineering (TE) is a field of research developing biological substitutes that restore, maintain, and emulate tissue functions. Its applications span regenerative medicine, pharmaceutical testing, and biological research. TE holds the promise of reducing reliance on animal testing, enabling personalized medicine, and addressing the shortage of donor tissues. Traditional monitoring methods in TE, such as histological analysis, are typically offline, destructive, and time-consuming. These methods require parallelizing the TE process to allow for periodic sampling, which increases costs and reduces efficiency. Moreover, they provide only inferred information about the whole population, lacking the ability to pinpoint and track dynamic biological processes in real time. Therefore, to fully realize the potential of TE, efficient, non-destructive, and scalable monitoring methods are required. Electrical Impedance Tomography (EIT) emerges as a promising tool in this context. EIT enables non-invasive, real-time monitoring of 3D cell cultures, which are central to TE. By providing continuous insights into tissue maturation, EIT can significantly enhance the efficiency and affordability of TE processes. Current EIT systems are originally designed for large-scale applications like lung imaging or geophysical exploration and suffer from a low signal-to-noise ratio and limited space resolution. Moreover, they usually exploit model-based reconstruction algorithms that linearize and simplify the inherently ill-posed inverse problem of EIT, leading to modeling errors and reduced image accuracy. Therefore, to fully benefit from EIT approach, the system must be adapted to meet the specific demands of TE. This project aims to realize the “next-generation EIT system for non-destructive monitoring of tissue-engineered construct” by addressing all the scientific and technological challenges for the application of EIT approach at the cellular level, such as improved sensitivity, enhanced image reconstruction accuracy, biocompatibility at the cellular level, and correlation analysis between conductivity measurements and tissue maturation. A multidisciplinary and multimethodological approach will be used to tackle the complexity of EIT systems by combining engineering, data science, non-linear mathematics, and biology. The specific case of mineralization of bone tissue constructs will be used as case study, targeting the detection and localization of calcium crystals created during the maturation process. Specifically, this project aims to investigate the limit of detection of EIT systems in terms of the minimal spatial resolution and minimal conductivity gradient resolution by linking global system requirements to specifications on the hardware level. Based on that study, low-noise electronic design will be exploited for the development of the EIT hardware platform. The project will also explore the benefits of using additive manufacturing electronic technology for the realization of the 3D-printed miniaturized tank in order to have a unique container with well-defined positions of the electrodes, as they are printed together with the non-conductive resin. Multi-modal and multi-frequency approaches will be analyzed to improve the final accuracy of the reconstructed image by enhancing the a-priori information about the space localization of the construct and the specific characteristics of the materials. The combination of EIT with multi-spectral optical imaging can improve the accuracy of the reconstructed image by following a sensing-fusion approach, while multi-frequency EIT measurement can exploit the different spectral responses of different materials to better localize the mineralized material. Finally, non-linear data-driven reconstruction algorithms based on variational methods will also be studied for improving the quality of the reconstructed images. The final ambitious goal of the project is the realization of a miniaturized EIT system capable of detecting a 1-mm3 object with a conductivity of 80 mS/m in a liquid background of 1.4 S/m, which corresponds approximately to 40 mM of calcium carbonate. Results The project achieved important results in the development of a new generation of Electrical Impedance Tomography (EIT), designed to observe the growth and maturation of engineered tissues without destroying them during analysis. The aim is to overcome one of the main limitations of the techniques currently most widely used in tissue engineering: to understand whether an artificial tissue is maturing correctly, it is often necessary to sacrifice a sample and analyze it using invasive methods. Instead, the project worked on a technology capable of “reading” the electrical properties of the biological construct from the outside, reconstructing useful information about its internal state in a continuous, cost-effective, and non-destructive way. In particular, the selected case study was the monitoring of mineralization in bone tissue constructs, a fundamental process for understanding whether cells are producing a bone-like structure. On the hardware side, several prototypes of miniaturized tanks and sensors were designed and fabricated, compatible with formats commonly used in biological laboratories. Configurations with electrodes distributed over one or more layers, as well as with a central electrode, were investigated to improve sensitivity even in the most difficult-to-observe regions. The project also defined a quantitative methodology to assess the system’s limit of detection, namely the smallest conductivity variation that can be distinguished from measurement noise. This result is particularly relevant because it enables the design of more reliable instruments and provides an objective understanding of which improvements are needed. In parallel, advanced algorithms were developed to transform electrical measurements into images and interpretable information. Among these, multifrequency methods and techniques based on neural networks and generative models showed, on synthetic data, an improved ability to reconstruct material distributions and maintain good performance even in the presence of noise. The project also explored the integration of electrical measurements with optical images, with the aim of combining different types of information to obtain more robust and understandable reconstructions. On the experimental side, alginate-based reference samples with different concentrations of calcium carbonate were prepared and used to simulate different levels of mineralization. The measurements showed a clear relationship between mineralization and conductivity: as the mineral content increased, conductivity decreased, confirming the possibility of using this parameter as an indicator of the biological process. At the same time, the experimental study highlighted critical issues and gaps in calibration and correction methodologies, as well as in the metrological traceability of impedance measurements in the biomedical field. Overall, the project built a solid technological, experimental, and mathematical foundation for making EIT a promising tool to monitor the growth of artificial tissues in real time, with potential impacts on regenerative medicine, biological research, and the development of alternatives to animal testing. The project had an excellent scientific impact, as demonstrated by the publication of five contributions to national and international conferences and three articles in scientific journals, either published or in press. In addition, the project led to new collaborations with the Technical University of Liberec and the Istituto Nazionale di Ricerca Metrologica, as well as to participation in a European project, Met4MED: Metrology for Medical Electrical Devices.
Project details
Unibo Team Leader: Marco Crescentini
Unibo involved Department/s:
Dipartimento di Ingegneria dell'Energia Elettrica e dell'Informazione "Guglielmo Marconi"
Coordinator:
ALMA MATER STUDIORUM - Università di Bologna(Italy)
Total Eu Contribution: Euro (EUR) 245.566,00
Total Unibo Contribution: Euro (EUR) 172.203,00
Project Duration in months: 24
Start Date:
30/11/2023
End Date:
28/02/2026
