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

关键项目成果包括确定区域特异性淀粉样蛋白负荷（由[18-F]氟替他莫PET脑成像测量）与从遗忘性轻度认知障碍（aMCI）到可能的阿尔茨海默病（pAD）的转化率之间的关联程度；此外，将涉及识别与认知能力下降密切相关的结构（多模态）MR成像生物标志物，并利用这些横断面PET-MR衍生成像措施来推动预测分析软件产品的开发。作为该项目的一部分，还将开发自动定量PET- mr神经成像处理和分析管道，以处理2009年一项多中心队列临床研究中随时可用的成像数据，在该研究中，232名出现aMCI的受试者在基线时接受了[18-F] PET脑扫描以及T1、T2和/或T2- flair MRI脑扫描，并在三年内定期检查他们的心理测量评分和转换状态。开发的预测分析工具将应用于病理证实的患者数据-通过从CSF获得的直接tau测量，或通过PET tau测量和心理测量数据；它的预测强度根据神经元破坏的程度来评估。因此，该项目将有助于加深对患者疾病轨迹的了解。预计研究项目的成果将促进更早和更有效的诊断，更好地指导临床干预战略————病人监测和护理规划，此外，它将有可能为临床医生有效评估病人是否适合/有资格参加新出现的临床试验提供基础，从而推进治疗领域的医学研究。该项目是牛津大学威康综合神经成像研究所（WINN）和通用电气（GE）医疗保健生命科学成像技术集团之间的合作成果；Mark Jenkinson博士和Christopher Buckley博士分别担任学术导师和行业导师，领导哲学博士研究项目。鉴于上述情况，该研究项目符合EPSRC医学成像/医学图像和视觉计算研究领域的优先事项1,3,6和7。

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.