Details of Grant 

EPSRC Reference:	EP/K036939/1
Title:	A Novel Diagnostic Tool: from Structural Health Monitoring to Tissue Quality Prediction
Principal Investigator:	Chen, Professor K
Other Investigators:	Wu, Professor J Chen, Dr Y Pankaj, Professor P
Researcher Co-Investigators:
Project Partners:
Department:	Mathematical Sciences
Organisation:	University of Liverpool
Scheme:	IDEAS Factory Sandpits
Starts:	01 October 2013	Ends:	31 March 2017	Value (£):	1,024,165
EPSRC Research Topic Classifications:	Continuum Mechanics Medical Imaging Non-linear Systems Mathematics
EPSRC Industrial Sector Classifications:	Healthcare
Related Grants:
Panel History:
Summary on Grant Application Form
As quality of life constantly improves, the average lifespan will continue to increase. The bad news is that tissue degradation due to wear and tear in an aged body is inevitable and is different from person to person. Fortunately recent advances in sciences and technology have enabled us to work towards personalised medicine this approach calls for a collective effort of researchers from a vast spectrum of specialised subjects. This project, by an interdisciplinary team from four different UK Universities with distinct areas of expertise, aims to predict patient-specific tissue quality which is essential in devising treatments plans. While our primary concern in this study is the bone tissue, the developed framework will apply to other tissues having porous or complex microstructure. To achieve the aim of the project, we have to overcome a few challenges. Firstly we need better mathematical models to extract the tissue microstructure accurately and automatically and also reliable mathematical methods for comparing two different samples from either a normal image (say with 1024 x 1024 pixels) or a 3D image containing shapes and embedded complex structures. Although one would expect that existing softwares can do these tasks, the reality is that these tasks are harder than we think as the images from a realistic scan contains noise. To counter noise, we use a novel mathematical technique called the variational method that actually constructs an energy quantity involving the whole image and minimises it. In doing so, a noise-free reconstruction is obtained and the precise location of the underlying tissue is identified. This project will develop efficient and robust models to extract local features only. Similarly we can construct different energies to compare two images in the so-called co-registration problem. Our new idea is to use more mathematical approaches and less statistical estimation ideas to achieve more accuracy and robustness. The CMIT research centre at Liverpool specialises in a range of mathematical models for different problem scenarios. Secondly once imaging extracts microstructural geometry, our team from Edinburgh and Heriot-Watt will develop and use a computational mechanics approach to evaluate the tissues' material properties. Since tissue quality depends on what its microstructure looks like, answers to questions such as: is it very porous, are different solid parts poorly connected and are most of the pores aligned in the same direction, should indicate how strong the tissue is. We will examine these microstructures and then conduct range of mechanical tests on the computer so as to obtain properties that tell us when and how the tissue will get damaged or fail. We then study how the microstructural geometry relates to the mechanical behaviour. We will use a process called homogenisation that will enable prediction of properties at macro (tissue) level from knowledge of micro level. Homogenisation to predict tissue failure has not been attempted before and presents a major challenge. Finally, the predicted properties must be validated from a combination of imaging and bespoke in-vitro experimentation. The validation process, to be done in Durham, will evaluate the accuracy of our models and provide model refinements. Once validated, our methodology can be used with confidence as a tool to evaluate tissue properties straight from images. Thus the proposed project contains both technical and advanced mathematical components and real applications in scenarios where non-invasive imaging is applicable. With imaging technology getting better, cheaper and more wide-spread, the prospects for novel applications of this work are immense.
Key Findings
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Potential use in non-academic contexts
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Organisation Website:	http://www.liv.ac.uk