Data-driven reconstruction algorithms for Magnetic Resonance Elastography

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Magnetic Resonance Elastography (MRE) is a powerful diagnostic imaging technique that measures changes in the biomechanical properties of biological tissue caused by disease. MRE research has recently begun at the Sir Peter Mansfield Imaging Centre, University of Nottingham, with the installation of an MRE system on the Philips 3-Tesla Ingenia Magnetic Resonance Imaging (MRI) scanner. MRE works by delivering mechanical waves to the tissue, which are measured using MRI, and these wave measurements are converted into estimated biomechanical properties using specialised reconstruction algorithms. These algorithms solve an inverse problem: starting from MR imaging data, they estimate tissue biomechanical properties, thereby allowing the differentiation of healthy and diseased tissue. The accurate identification of the disease location and boundaries is a main challenge for current reconstruction algorithms, which are required to assimilate a large amount of noisy MRI imaging data. The objective of this project is to develop and validate novel reconstruction algorithms for MRE data using state-of-the-art Bayesian inversion techniques. The aim of these algorithms is to enable accurate reconstruction of tissue properties, and to quantify uncertainties in estimated/reconstructed properties. These algorithms will be tailored to improve significantly MRE-based diagnosis, by assimilating MRI data into a general class of heterogeneous and anisotropic biomechanical models. The validation of the algorithms developed with this project will be conducted with data acquired at the Sir Peter Mansfield Imaging Centre.