A cross-resolution preclinical imaging framework based on MRI and light sheet microscopy to assess post-stroke axonal regrowth

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Magnetic Resonance Imaging (MRI) is an important diagnostic and research tool that provides detailed images of the brain non-invasively. However, it is limited by resolution (approx. 1mm). The development of models that can link features of the image to underlying cellular information at the micrometre scale would significantly improve specificity and sensitivity of diagnosis and help to tailor treatments to individual patients. We aim to develop such a model by directly comparing MRI to three-dimensional microscopy. Then, we will test the framework on a stroke animal model. Stroke is the second cause of death worldwide and produces a complex pattern of brain injury followed by various degrees of cellular regrowth. It is therefore an optimal clinical case to validate our model, leading to the development of accurate and precise imaging-based biomarkers.The student will engage in specialist training with ex vivo and in vivo (subject to the experience and wishes of the applicant) animal handling, preclinical imaging, and data analysis techniques. The successful candidate will also acquire new computational and modelling skills as well as learn practical laboratory procedures to make a brain transparent. This will allow the student to model features of the MR images using 3D microscopy images.