Robust Repeatable Respiratory Monitoring with EIT

Lead Research Organisation: King's College London
Department Name: Asthma Allergy and Lung Biology


Mechanical ventilation in the intensive care unit (ICU) is often a life-saving intervention for patients suffering from acute respiratory distress syndrome (ARDS) and acute lung injury (ALI). However, in recent years, it has become clear that mechanical ventilation can exacerbate lung damage and may even be the primary factor in lung injury e.g. ventilator-induced lung injury (VILI) and ventilator associated pneumonia (VAP). Consequently, lung protective ventilation (LPV) strategies have been developed, including positive end expiratory pressure (PEEP) and high frequency oscillatory ventilation (HFOV). Despite the use of LPV, the mortality associated with ARDS and ALI is still great, with reported rates between 33 and 55%.

Thoracic electrical impedance tomography (EIT) has emerged as a promising new imaging tool for bedside use. It is a non-invasive technique which provides the internal conductivity distribution based on electrical measurements on the skin. Existing research and commercial EIT systems have typically arranged measurement electrodes in rings around the thorax and produced two-dimensional image slices based on over-simplified two-dimensional generic shapes e.g. circular or elliptical cross-sections. Unfortunately, such an approach creates significant image artefacts and poor levels of repeatability. In order to eliminate this problem, EIT measurements and image reconstruction must be treated as a fully three-dimensional problem, taking into account the electrode positions and the body shape of the patient. One of the aims of this project is to provide reliable and robust three-dimensional EIT image reconstruction based on 3D informed body shape acquired from other modalities such as magnetic resonance imaging (MRI) and x-ray computed tomography.

Currently, there exists no consensus amongst clinicians on how to optimize PEEP ventilator settings. Improved knowledge about the distribution of ventilation will enable clinicians to set more appropriate ventilation parameters in order to reduce the potential occurrence of VILI. In particular, the value of bedside EIT monitoring for determining the effect of PEEP in comparison with traditional indirect methods, such as arterial oxygenation or global tidal volumes, is still largely unknown. Additionally, despite the clear link between lung mechanics and regional ventilation, ventilator settings are normally based on blood gases and pressure-volume curves rather than measurements of lung mechanics. A significant aim of this project is to provide clinicians with robust image-based lung mechanics models which will further aid the validation of EIT in the ICU.
Previous clinical EIT studies have generally utilized EIT instruments operating at relatively slow frame rates, inflexible drive and measurement strategies along with limited number of electrodes. In this project, we will develop a state-of-the-art fast 3D EIT system using advanced measurement techniques which have not yet had a significant impact in the clinical environment. In fact, there has never been a substantial investigation of EIT in the ICU which has drawn together the unique combination of high quality EIT measurements, three-dimensional image reconstruction and the validation of lung mechanics modelling.

We propose to establish a world-leading capability in the measurement and imaging by EIT in the ICU. This project builds upon the substantial expertise in EIT at the University of Manchester (UoM) and the UK's leading clinical group in applied thoracic EIT at Kings College London and Guy's and St Thomas' NHS Trust. We will also build upon the world-leading capability at UoM in modelling and dynamic MRI acquisition including oxygen-enhanced and dynamic contrast-enhanced MRI. The proposed project registers strongly on the EPSRC research theme of healthcare technologies.


10 25 50
Description A new system has been developed for monitoring lungs using electrical impedance and it is undergoing testing. Progress has been made on reconstruction algorithms including automatic segmentation and meshing based on lung images. The feasibility of using EIT data for automatic control of ventilation has been demonstrated in simulations.
Exploitation Route We are moving forward to testing the systems on volunteers and patients
Sectors Electronics,Healthcare

Description The preparatory clinical data collection component has facilitated more structured imaging and physiological assessment of critically ill patients with severe respiratory failure. This is an EIT system developed for clinical use and optimized for respiratory monitoring. It is currently in the process of MHRA approval so that it can be tested in Guy's and St Thomas's Hospital ICU. If successful, this will reinforce existing standards of care, improve diagnosis and improve/accelerate research approaches. As a combined team, we have improved EIT algorithms including theory behind respiratory control using an EIT system. We have also developed a software r system for inputting the body shape and segmenting organs from a DIcom image, which is then used to generate a FE mesh for EIT with correct body shape and electrode positions. The MIRAN segmentation software our partner team members developed with NE Scientific has been released and is being used.
First Year Of Impact 2018
Sector Healthcare
Description Partnership with University of Gottingen 
Organisation University of Göttingen
Country Germany 
Sector Academic/University 
PI Contribution Developing relationship on advanced respiratory monitoring with the University of Gottingen
Collaborator Contribution Developing relationship in the field of advanced monitoring of respiratory function in critically ill patients with severe respiratory failure, with our group represented by Dr Luigi Camporota, and our partners by Prof Miachael Quintel and Prof Luciano Gattinoni
Impact Ongoing work on animal model of healthy lungs ventilated with different mechanical power. EIT used to evaluate, lung water, lung homogeneity and distribution of ventilation using commercial devices but off line analysis of data using dedicated research algorithms and EIDORS models. This collaboration is now testing real life clinical conditions (ARDS) in patients undergoing mechanical ventilation and ECMO.
Start Year 2016
Title R3M 
Description The overall project is structured in four different phases comprising an initial engineering and mathematical phase (during which EIT electrodes are designed and built, and a software algorithm is developed) of a later phase, with clinical involvement: a clinical bench to bedside testing of the device and software algorithm developed. The project is now approaching the end of the initial engineering phase. However, KCL is contributing to this initial phase by acquiring electrical impedance tomography images - using a commercially available system and analysis tool- and pairing them to anonymised chest CT images, obtained in a protocolised fashion in tightly defined severe ARDS patient population. This allows better definition of the geometrical parameters necessary to build the new software and better define pattern of electrode placement." 
Type Of Technology New/Improved Technique/Technology 
Year Produced 2015 
Impact Enabling ongoing project work