NeuRA Magazine #20


Dr Lauriane Jugé with a device used as part of the study

When human tissue, such as muscles or some organs, are affected by disease, they can become stiffer than surrounding muscle tissue. Traditionally, medical practitioners have used the palpation technique – using their hands to determine the firmness of tissue, for instance around the abdomen – to feel for changes in tissue stiffness in order to diagnose illness or disease.

While this is an effective technique, not all tissue is accessible to a physician’s hand. In these cases magnetic resonance elastography (MRE), a non-invasive medical imaging technique, has been developed to assess the stiffness of tissue such as the brain.

Prof Lynne Bilston’s team, which includes Dr Lauriane Jugé is particularly interested in changes in tissue stiffness in the brain and muscles and how this changes in neurological and muscle disorders. During their research they came to realise that while there is a lot of data on stiffness in adult tissue, there was little to none when it came to children. To answer this they are working on new methods to measure the properties of tissue when it is in use or under stress, either as a result of accident or disease.

One of the areas they are particularly interested in studying involves keeping children safe during car accidents. Current injury criteria and anthropomorphic test dummies, for example, are based on scaling adult anatomy to match children’s anatomy. Despite this, the dummies use adult tissue properties, even thought there is evidence that this can result in flawed injury criteria that cannot predict injury outcome in real children.

One of their current studies involves using MRE and diffusion tension imaging to find a more accurate way to assess and measure soft tissue changes in children. In doing this they hope to be able to quantify the mechanical properties and microstructure of tissues in healthy children in order to better predict the responses of these tissues in situations such as car accidents or disease. They’re confident that they will be able to fill in the critical gap in knowledge to so they can create accurate computational models of the body for use in child injury prevention, and other medically-related fields.

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