Lead Research Organisation: Middlesex University
Department Name: School of Science and Technology


This research proposes the development of an urgently needed Electrical Impedance Tomography (EIT)-based bio-imaging system for in-vivo monitoring of neonate lung function in Intensive care Units (ITUs). Disorders of lung growth, maturation and breathing control are among the most important problems faced by the neonatologist. Premature birth occurs in 5-10% of all pregnancies, and is frequently accompanied by complications due to lung immaturity. The annual cost to the NHS is estimated in the order of hundreds of millions. One of the key difficulties of neonate ITU monitoring for lung function is that the system must measure the subject in a passive manner, unlike adults who can be asked to perform specific respiratory tasks. EIT is the only solution to this problem. Moreover, EIT is proving to be successful in many clinical applications, an example is the new commercial TSCAN system for detection of breast cancer.The proposed system will provide a non-invasive measure of lung maturity and development, oxygen requirements and lung function, suitable for use in small, unsedated infants. This tool will be routinely used to define the nature and severity of persisting lung disease, and to identify risk factors for developing chronic lung problems. Major limitations of EIT are the inherent common mode errors which result in reduction of the quality of the reconstructed images. These errors are more pronounced in the case of multi-frequency EIT. We have invented a method to address the problem by the use of a novel wideband analogue common-mode enhancement technique which can maximise the overall common-mode rejection ratio (CMRR) and signal-to-noise ratio (SNR). To take full advantage of the capabilities of our wideband CMRR feedback approach, we will use full custom circuit design techniques to create a novel multi-frequency EIT system-on-a-silicon-chip. We have also developed advanced reconstruction methods involving new mesh warping techniques that utilise the boundary information to condition the forward model, taking into account the anatomical details of the problem domain. The end result of this research will be new technology in the form of a wearable device which will minimise disruption when monitoring neonates. This will involve the design of custom made silicon chips, an integrated wearable electrode system, new 3D image reconstruction algorithms and advanced post image processing methodologies. This research will be carried out with the full clinical support of the Portex Anaesthesia, Intensive Therapy and Respiratory Medicine Unit at the world leading children's Hospital, Great Ormond Street, London.
Description The results show some improvement in separation of both lungs by using improved estimates of the boundary form. Approximating the boundary for using and ellipse or simple 2-D form produce more artefacts in the image. We experimented with a range of SVD's and produce no improvement in the reconstructed images.
Figure (a) inplane image has minimal artefact however the out of plane (b) show an increase in lung size and artefact.

It is suggest that inclusion of the full boundary form will be required. We are currently investigating the use of multiple ring of electrode
Exploitation Route By further funding to developed the prototype system
Sectors Aerospace, Defence and Marine,Electronics,Healthcare

Description Both Prof. Bayford and Dr Tizzard developed the research underpinning the impact on the development of the reconstruction algorithms and the forward models for EIT of the human brain. This led to key to the image improvements in the reconstruction algorithms, which produce the clinical images. The initially research that was undertaken by Prof Bayford focus on the development of imaging of impedance change inside the human brain which previously had not been achieved with this method. It was clear from this research that if EIT was to be used in the clinical area, then artefacts generated in the image by using the incorrect shape of the anatomical structure, specifically the boundary form (shape.). Correct boundary form plays a major factor in reducing artefacts in the reconstructed image. This is particularly important as these artefacts could be misinterpreted as clinically relevant information and limit the application of this low cost non invasive imaging modality. The group at Middlesex University has extended its research to address a number of clinical areas that including Brain function, Breast/Rectal cancer and Neonate and child lung imaging. The technique will significantly reduce the cost of patient care and mortality by allowing systematic monitoring of child lung function for time-critical intervention. Considerable interest has grown in the use of EIT for safe monitoring of lung function (Bayford. 2006, Wolf & Arnold 2006). In particular there is an urgent need to improve ventilation strategies in children with acute lung injury (ALI), which has a high mortality (22%) compared with the overall mortality of paediatric intensive care unit patients. Following on from the above research, the team at Middlesex turned its attention (2006) to the application of this technology to neonate lung imaging. Disorders of lung growth, maturation and control of breathing are among the most important problems faced by the neonatologist. Premature birth occurs in 5-10% of all pregnancies, and is frequently accompanied by complications due to lung immaturity. Many preterm infants exhibit lung dysfunction characterised by arrested lung development and interrupted alveolarisation. This immature lung phenotype accounts for 75% of early mortality and long-term disability in infants delivered prematurely. Despite improved survival of extremely premature (EP) infants i.e. those born < 27w gestational age [GA]), the prevalence of chronic lung disease in infancy (CLDI; commonly defined by oxygen (O2) dependence at 36 weeks post-menstrual age [PMA] i.e., 4w before the baby was due to be born), has remained high over the last decade. CLDI is associated with long-term, and possibly life-long, respiratory morbidity. Objective, non-invasive measures of lung maturity and development, oxygen requirements and lung function, suitable for use in small, unsedated infants, are urgently required to define the nature and severity of persisting lung disease, and to identify risk factors for developing chronic lung problems. Although it is not yet used to monitor lung function in neonates, it is being used for adult patients using some of the development create for neonate imaging. Through collaboration with clinicians, two key papers have been produced demonstrating this impact. (GREIT: a unified approach to 2D linear EIT reconstruction of lung images", Physiol Meas, 30:S35-S55, 2009 and Whither lung EIT: where are we, where do we want to go, and what do we need to get there? Impress 2012) Both of these papers were influenced by the work at Middlesex and has resulted in the generation of software that contributed to the EIDORS project (eidors3d.sourceforge.net/). This project is a international collaboration to provide algorithms for forward and inverse modelling for Electrical Impedance Tomography (EIT) and Diffusion based Optical Tomography and is being used to make clinical measurements. The concept of the GREIT paper referenced is based on the idea that Electrical Impedance Tomography (EIT) is an attractive method for clinically monitoring patients during mechanical ventilation, because it can provide a non-invasive continuous image of pulmonary impedance which indicates the distribution of ventilation. However, most clinical and physiological research in lung EIT is done using older and proprietary algorithms; this is an obstacle to interpretation of EIT images because the reconstructed images are not well characterized. To address this issue, we are developing a consensus linear reconstruction algorithm for lung EIT, called GREIT (Graz consensus Reconstruction algorithm for EIT). The describes the unified approach to linear image reconstruction developed for GREIT. The framework for the linear reconstruction algorithm consists of: 1) detailed finite element models of a representative adult and neonatal thorax; 2) consensus on the performance figures of merit for EIT image reconstruction; and 3) a systematic approach to optimize a linear reconstruction matrix to desired performance measures. Consensus figures of merit, in order of importance, are: a) uniform amplitude response. All software and data to implement and test the algorithm has been made available under an open source license which allows free research and commercial use and is been adopt by manufactoter of EIT systems. The groups research has also pioneered a mesh wrapping technique to accountant for errors boundary form which was applied to both Brain, Breast and lung imaging. The work has been used to improve clinical images of Breast in Dartmouth in the US.
First Year Of Impact 2011
Sector Electronics,Healthcare
Impact Types Societal,Economic

Description H2020
Amount € 5,500,000 (EUR)
Organisation European Commission 
Department Horizon 2020
Sector Public
Country European Union (EU)
Start 01/2016 
End 01/2019
Description An apparatus (480) for use in estimating the shape of a body part of a subject, which apparatus (480) comprises: a string of sensors (481) for positioning adjacent the body part so that the string of sensors substantially conforms with the shape of the body part, or at least a part of it, the string of sensors comprising at least one bend sensor and at least one stretch sensor (482) arranged end to end such that, in use, the at least one bend sensor lies adjacent a first region of the body part and the at least one stretch sensor (482) lies adjacent a second region of the body part, wherein the magnitude of a curvature of the first region of the body part is greater than the magnitude of a curvature of the second region of the body part. 
IP Reference WO2015025113 
Protection Patent application published
Year Protection Granted 2015
Licensed No
Impact Considerable improvement in clinical image quality