Towards a better understanding of cardio and cerebrovascular diseases

项目背景：MRC终身健康和老龄化单位(LHA)是一个跨学科的单位，我们的研究结合了来自整个生命过程的丰富的成像、临床、生理和生物数据，以帮助我们发现正常和病理性的衰老过程。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的专业知识，与临床医生合作开发应用于医疗成像的技术。学生将接受计算机科学家、临床医生和数据科学家的指导意见，并完全融入这两个单元/中心。有四个研究项目可获得资金，每个项目都侧重于人类健康和疾病的重要方面。一般而言，每个项目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，总体目标是改善对心脑血管疾病和认知衰退的预防，并促进健康老龄化。研究目标：每个项目都将涵盖以下目标之一：发现医学成像的新特征(如超声心动图、磁共振)，以预测主要临床结果(如心脏病发作、住院、使用新的远程监测技术(如可穿戴设备和移动健康设备)来确定对长期心血管健康有重大影响的趋势。根据脑部MRI成像和临床数据开发新的脑血管疾病演变预测模型。提高对脑血管病变和认知之间可能联系的理解

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