Disease staging from amnestic mild cognitive impairment to probable Alzheimer's disease via MRI and[18F]flutemetamol PET

点击翻译按钮获取中文摘要

Key project deliverables include determination of the degree of association between region-specific amyloid burden as measured by [18-F] Flutemetamol PET brain imaging and the rate of conversion from amnestic Mild Cognitive Impairment (aMCI) to probable Alzheimer's Disease (pAD); furthermore, will involve the identification of structural (multi-modal) MR imaging biomarkers strongly correlated with cognitive decline, and utilising these cross-sectional PET-MR derived imaging measures to drive the development of a predictive analytic software product. An automated quantitative PET-MR neuro-imaging processing and analysis pipeline will also be developed as part of this project to process imaging data readily available from a 2009 multi-centre cohort clinical study during which 232 subjects presenting aMCI underwent a [18-F] PET brain scan along with a T1, T2 and/or T2-FLAIR MRI brain scan at baseline, and their psychometric score and conversion status reviewed periodically over three years. The developed predictive analytic tool will be applied to pathologically confirmed patient data - via direct tau measurements obtained from CSF, alternatively via PET tau measures and psychometric data; and its predictive strength evaluated with respect to the extent of neuronal disruption. The project will, therefore, serve to deepen insight into patients' disease trajectory. It is anticipated that the output of the research project will facilitate earlier and more effective diagnosis, better guiding clinical intervention strategies - patient monitoring and care planning, moreover, it will potentially provide a foundation on which clinicians can effectively assess patient suitability / eligibility for emerging clinical trials and thus advancing medical research within the treatment domain. The project is a collaborative effort between the Wellcome Institute for Integrative Neuroimaging (WINN), University of Oxford and the Imaging Technology Group of General Electric (GE) Healthcare Life Sciences; with Dr Mark Jenkinson and Dr Christopher Buckley leading the DPhil research project in the role of academic and industrial supervisors, respectively. In light of the above, the research project corresponds to priorities 1, 3, 6 and 7 of the EPSRC's Medical Imaging / Medical Image and Vision Computing Research Area.