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，它可以导致短暂性脑缺血发作、中风和血管性痴呆。显性病理的前兆是血管逐渐硬化，由于破坏性脉动压力波进一步深入组织床，血管本身可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)，非侵入性地评估血管僵硬已经成为可能，尽管迄今为止大多数测量方法是全身性的。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非侵入性MRI方法。与仅提供全身平均值的传统测量方法相比，这种更具体的血管硬度测量方法与其他神经血管和弥漫性脑组织疾病的相关性应该要好得多。成功的学生将具有物理、工程、计算机科学或相关领域的背景，并将有兴趣将他们的培训应用于医疗应用。将开发和优化结合多层同时采集的高空间和时间分辨率相衬磁共振血管成像序列，以采集整个大脑多个血管位置的心脏波形。将研究和优化分析方法，以测量在颅内血管树不同点收集的PWV波形之间存在的微妙时间移位，包括研究使用来自潜在采集的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，这些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之间进行验证。

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.