Towards a better understanding of cardio and cerebrovascular diseases

项目背景：MRC终身健康与老龄化部门（LHA）是一个跨学科的部门，我们的研究结合了丰富的影像、临床、生理和生物数据，从整个生命过程中帮助我们发现正常和病理衰老过程。医学图像计算中心汇集了数学和计算科学以及医学物理学方面的专业知识，与临床医生合作开发应用于医疗保健成像的技术。学生将接受计算机科学家、临床医生和数据科学家的指导，并完全融入两个单位/中心。资金可用于四个研究项目，每个项目侧重于人类健康和疾病的重要方面。从广义上讲，每个项目都将涉及将数据科学和机器学习技术应用于生理和成像数据，以更好地了解正常衰老和疾病过程，其总体目标是改善心脑血管疾病和认知能力下降的预防，并促进健康老龄化。研究目标：每个项目将涵盖以下目标之一：发现医学成像（如超声心动图，磁共振）的新特征，以预测主要临床结果（如心脏病发作，住院，死亡）。使用新颖的远程监测技术（例如可穿戴设备和移动保健设备）确定对长期心血管健康有重大影响的趋势。基于脑MRI成像和临床数据，开发新的脑血管疾病演变预测模型。提高对脑血管病变与认知之间可能联系的认识

Project Background: The MRC Unit for Lifelong Health and Ageing (LHA) is an interdisciplinary unit and our research combines rich imaging, clinical, physiological, and biological data from across the life course to help us lead on discoveries into both normal and pathological ageing processes. The Centre for Medical Image Computing gathers expertise from mathematical and computational sciences and medical physics to develop technologies applied to healthcare imaging in partnership with clinicians. Students will receive supervisory input from computer scientists, clinicians and data scientists and be integrated fully into the two units/centres. Funding is available for four research projects each focusing on important aspects of human health and disease. Broadly, each project will involve applying data science and machine learning techniques to physiological and imaging data to gain better insight into normal ageing and the disease process, with the overarching aim of improving prevention of cardio- and cerebro-vascular disease and cognitive decline and promoting healthy ageing.Research aims: Each of the projects will cover one of the following aims:Discover novel features in medical imaging (e.g. echocardiography, magnetic resonance) to predict major clinical outcomes (e.g. heart attack, hospital admission, death).Use novel remote monitoring techniques (e.g. wearable devices and mobile Health devices) to identify trends significantly impacting on long-term cardiovascular health.Develop new predictive models of cerebrovascular disease evolution based on brain MRI imaging and clinical data.Improve the understanding of the possible links between cerebrovascular lesions and cognition