Creating high fidelity digital tissue substrates for the development of non-invasive microstructural MRI

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Cancer is one of the developed world's leading health care challenges for the 21st Century. Developing new imaging techniques that allow suspected cancers to be diagnosed without surgery, or determine early on whether a treatment is working, are important ways we may tackle this challenge.Magnetic resonance imaging (MRI) is a technique that is already widely used in hospitals. MRI is often used in cancer to see if a tumour is growing or shrinking in response to treatments like chemotherapy or radiotherapy. New ways to use MRI are always being developed; one new development is a technique called microstructural MRI. This technique allows researchers and clinicians to measure how the structure of tissues in the body are changing. For example, measuring the size of cells, or the orientation of blood vessels in a particular section of tissue. These measures of tissue structure are an important indication of healthy function or disease progression. In diseases like cancer, samples or biopsies of a tumour are taken by a surgeon. The biopsy is looked at under a microscope by a histologist who can use the shapes and sizes of the cells and blood vessels to diagnose or grade a cancer's severity. Being able to make these measurements with MRI would mean that tumour biopsies could be done easily without surgery. In addition, subtle changes in a tissues structure can indicate that a tumour is responding to a particular treatment long before a conventional MRI would detect tumour shrinkage. Using microstructural MRI for this purpose could help to identify treatments which aren't working, and allow them to be changed more quickly, hopefully leading to a better outcome for that patient.Microstructural MRI has already been tried in the clinic with some success, however its use so far has highlighted that certain tissue structure measurements seem to have low reproducibility or vary in unexplained ways. I am aiming to investigate this variability. The way in which a tissue's structure can be found using MRI relies on a computer model to determine what tissue structures would have produced the MRI signal that is acquired. In order to investigate why particular outputs are varying, data are needed where the actual tissue structure or ground truth is known and can be compared to the MRI outputs. Ideally, this known tissue structure would be one that could be changed in a precise way to then see how the known change has affected the MRI output. However, acquiring these known tissue structures or ground truths is very difficult.To get around this problem of missing ground truth data researchers have used virtual MRIs. A virtual MRI can take any digitally constructed shape and simulate the MRI you would acquire from it. These digitally constructed shapes can be made to look like tissues and can be easily manipulated by to investigate certain types of changes in tissue structure. Up till now researchers have used very simple digital shapes to represent tissues, for example a collection of perfect spheres to represent cells and cylinders to represent blood vessels. This does not represent the complexity of real tissue and many researchers believe this may be the reason for the difficulties in the clinical trial thus far.My fellowship aims to create a library of these digital tissues which are based on the shapes I will extract from real tissue. To do this I will use a 3D imaging technique that I have developed. It uses a microsope to take 3D images of tissues at the scale of single cells for whole small tumours (~2cm3). I aim to extract the shapes of the cells and blood vessels from these images which I will then turn these into digital versions through a computer model. These digital tissues can then be used to conduct many virtual MRIs to understand the complex relationship between the MRI signal and the underlying tissue structure. This approach combines the flexibility of digital tissues with the complexity of real tissue structure.