Motion robust quantitative MRI of the brain at 7T

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1) Brief Description of the context of the research including potential impactThis project will be addressing the effects of motion on quantitative MRI of the brain at 7T. In collaboration with Siemens, we have previously shown that motion is problematic not only because of encoding errors, but also because of position-dependent transmit and receive fields (1,2). Eliminating these problems would benefit clinical studies that seek to identify laminar-specific signatures of neurodegeneration, including such studies of Huntington's Disease, familial Alzheimer's Disease, Parkinson's and Epilepsy running locally.2) Aims and ObjectivesTimelineYear 1: The student will have completed their Master of Research with taught modules encompassing medical imaging and AI, as well as a self-contained research project that will assess the utility of low rank reconstructions.Year 2: The student will commence the PhD itself and build on the work from the previous year by revisiting how the data are acquired and define optimal sampling schemes that minimise acquisition times, and therefore the risk of intra-scan motion, while preserving image quality. The student will also start to formulate the generative model of the data incorporating contrast-specific transmit and receive field effects that allow for inter-scan motion. An updated protocol will be deployed.Year 3: The student will assess how sampling schemes might change across contrasts and be used in a joint reconstruction to further mitigate intra-scan motion-sensitivity while ensuring accurate and reproducible quantification of multi-parameter maps. The generative model will be trained to learn the covariance structure of the data such that it would then be able to impute values in novel data.Year 4: The generative model will be used to compensate for for inter- and intra-scan motion to produce a final deployable protocol. The thesis will be written and all publications finalised (if not already completed).3) Novelty of Research MethodologyThe project will exploit the fact that quantitative MRI is comprised of multiple acquisition and establish a generative model of these data, and their covariance, in order to exploit the redundancy that this affords.4)Alignment to EPSRC's strategies and research areasThe objectives of this project have the potential to have a world-class impact in transforming healthcare; enhancing quality and capabilities of the high-field MRI, where these solutions can be applied to lower-field MRI as well.5)Any companies or collaborators involvedThe project will be conducted in close collaboration with Siemens Healthineers, who are part-funding the studentship.References1. Papp D, Callaghan MF, Meyer H, Buckley C, Weiskopf N. Correction of inter-scan motion artifacts in quantitative R1 mapping by accounting for receive coil sensitivity effects. Magnetic Resonance in Medicine. 2016;76(5):1478-1485. doi:10.1002/mrm.260582. Balbastre Y, Aghaeifar A, Corbin N, Brudfors M, Ashburner J, Callaghan MF. Correcting inter-scan motion artifacts in quantitative R1 mapping at 7T. Magnetic Resonance in Medicine. 2022;88(1):280-291. doi:10.1002/mrm.29216"