Development of Methods to Assess Intracranial Vessel Stiffness as a Measure of Vascular Disease

血管疾病是死亡和发病的主要原因，导致许多器官系统的病理。在大脑中，它可导致短暂性脑缺血发作、中风和血管性痴呆。明显病理学的前兆是血管的逐渐硬化，其本身可由于进一步到达组织床中的破坏性脉动压力波而导致弥漫性病理组织损伤。通过测量心脏压力波通过血管树时的脉搏波速度（PWV），可以无创评估血管硬度，尽管迄今为止大多数测量方法都是系统性的，本项目旨在通过测量大脑主要动脉内的PWV，开发专门针对颅内血管的无创MRI方法。与仅提供全身平均值的传统测量相比，这种更具体的血管硬度测量与其他神经血管和弥漫性脑组织疾病的相关性要好得多。成功的学生应具有物理学、工程学、计算机科学或相关领域的背景，并且有兴趣将他们的培训应用于医学应用。高空间和时间分辨率相位-将开发和优化包含同时多切片采集的对比MRI血管造影序列，以在整个大脑的多个血管位置处对心脏波形进行采样。将对分析方法进行研究和优化，以测量在颅内血管树中不同点收集的PWV波形之间存在的细微时移，包括研究使用基础采集同时性质的先验信息的组合时空图像重建算法。将开发其他分析方法，以根据血管造影数据对血管树中关键节点之间的血管路径长度进行建模。最后，该方法将在有证据表明存在小血管疾病的患者与健康受试者中进行验证。

Vascular disease is a major cause of mortality and morbidity, leading to pathologies across many organ systems. In the brain it can lead to transient ischaemic attack, stroke, and vascular dementia. A pre-cursor to overt pathology is a gradual stiffening of blood vessels, which itself can lead to diffuse pathological tissue damage due to damaging pulsatile pressure waves reaching further into the tissue bed. Non-invasive assessment of vascular stiffness has been possible through measurement of the pulse wave velocity (PWV) of the cardiac pressure wave as it passes through the vascular tree, although most measures to date are systemic in nature.This project seeks to develop non-invasive MRI methodologies that target the intracranial vasculature specifically, by measuring PWV within the brain's major arteries. This more specific measure of vessel stiffness should correlate much better with other neurovascular and diffuse brain tissue diseases than conventional measures that only provide a whole-body average.The successful student would have a background in physics, engineering, computer science or a related field, and would have an interest in applying their training to a medical application.High spatial and temporal resolution phase-contrast MRI angiography sequences that incorporate simultaneous multi-slice acquisition will be developed and optimized to sample the cardiac waveform at multiple vessel locations throughout the brain. Analysis approaches will be investigated and optimized to measure the subtle time shifts present between the PWV waveforms collected at different points in the intracranial vascular tree, including the investigation of combined spatio-temporal image reconstruction algorithms that use prior information from the simultaneous nature of the underlying acquisition. Additional analysis methods will be developed to model from angiographic data the vascular pathlengths between key nodes in the vascular tree. Finally, the methods will be deployed for validation in patients with evidence of existing small vessel disease versus healthy subjects.