GENIUS: Grid Enabled Neurosurgical Imaging Using Simulation

GENIUS：使用模拟实现网格神经外科成像

• 批准号: EP/F00561X/1
• 负责人: Peter Coveney
• 金额: $ 20.01万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF00561X%2F1
• 关键词: GENIUS; Grid; Enabled; Neurosurgical; Imaging

Cardiovascular disease is the cause of a large number of deaths in the developed world; conditions such as arterio-venous malformations and aneurysms can lead to strokes and death. Problems are often related to anomalous cerebral blood flow behaviour in regions of arterial branching within the brain; therefore, control of cerebral blood flow is crucial in the understanding, diagnosis and treatment of cardiovascular disease. Currently such flow details are not well understood, while experimental studies of cerebral blood flow are often impractical due to the difficulties in measuring real time blood flow in humans. X-ray and magnetic resonance imaging angiography (MRA) are non-invasive ways to produce static images of cerebral arterial structure, but have difficulty producing dynamic images due to the time scales required to obtain the images, and the need to inject the patient with tracer fluids.Some studies have revealed relationships between specific flow patterns around walls and cardiovascular diseases such as atherosclerosis. Current imaging methods represent a very important tool for diagnosis of various cardiovascular diseases and for the design of cardiovascular reconstructions and devices to enhance blood flow. The development of techniques which produce more accurate dynamical information on the flow of fluids in the blood within the brain will give the surgeon much greater success rates when treating cerebral blood flow pathologies, for example when re-routing flow and injecting cross-linking epoxy resins to block off channels.Modelling and simulation have a crucial role to play in neurosurgical blood flow treatments, due to the limitations of experimental methods. Simulation offers the clinician the possibility of performing non-invasive virtual experiments in order to plan and study the effects of certain courses of treatment with no danger to the patient. Modelling and simulation offer the prospect of providing clinicians with virtual patient specific analysis and treatments. Achieving these goals is dependent on the availability of computational models of sufficient complexity and power. In this project we will use the brain imaging techniques discussed above to provide input data for such simulations. The computational requirements to perform such simulations are huge in terms of the memory and number of processors required, meaning that we have to distribute them across multiple high performance supercomputers. Using our home grown application for modelling cerebral blood flow, HemeLB, we will perform such simulations on a federated grid of supercomputers, using resources provided by both the UK and the US; we shall take advantage of fast network links to enable efficient communication between these resources. We will also use tools to allow us to steer and visualise the simulations as they are in progress. We will take advantage of the ability to co-reserve time on these distributed machines to allow us to launch simulations as and when we require. Such a capability is essential to engage clinicians with high performance computing, allowing them to perform simulations and use resources at times convenient for them. We believe that this project will provide a prototype brain blood flow modelling environment that will be of considerable value to clinicians; we will continue to work beyond the scope of this project to exploit this technology for use in everyday surgical procedure planning.

心血管疾病是发达国家大量死亡的原因;动静脉畸形和动脉瘤等疾病可导致中风和死亡。这些问题通常与脑内动脉分支区域的异常脑血流行为有关;因此，控制脑血流对于理解、诊断和治疗心血管疾病至关重要。目前，这种流动的细节还没有很好地理解，而脑血流的实验研究往往是不切实际的，由于在人体内测量真实的时间血流的困难。X射线和磁共振成像血管造影（MRA）是产生脑动脉结构的静态图像的非侵入性方法，但是由于获得图像所需的时间尺度以及需要向患者注射示踪流体而难以产生动态图像。一些研究揭示了壁周围的特定流动模式与心血管疾病（例如动脉粥样硬化）之间的关系。目前的成像方法代表了用于诊断各种心血管疾病以及用于设计心血管重建和装置以增强血流的非常重要的工具。在治疗脑血流病变时，例如在改变血流路线和注射交联环氧树脂以堵塞通道时，产生关于脑内血液中流体流动的更准确的动态信息的技术的发展将使外科医生获得更大的成功率。建模和仿真在神经外科血流治疗中起着至关重要的作用，由于实验方法的局限性。模拟为临床医生提供了进行非侵入性虚拟实验的可能性，以便计划和研究某些治疗过程的效果，而不会对患者造成危险。建模和仿真提供了为临床医生提供虚拟患者特异性分析和治疗的前景。实现这些目标是依赖于计算模型的足够的复杂性和权力的可用性。在这个项目中，我们将使用上面讨论的大脑成像技术来为这种模拟提供输入数据。执行此类模拟的计算需求在所需的内存和处理器数量方面是巨大的，这意味着我们必须将它们分布在多个高性能超级计算机上。使用我们自己开发的用于模拟脑血流的应用程序HemeLB，我们将使用英国和美国提供的资源，在超级计算机的联合网格上进行此类模拟;我们将利用快速网络链接来实现这些资源之间的有效通信。我们还将使用工具，使我们能够在模拟过程中进行操纵和可视化。我们将利用在这些分布式机器上共同保留时间的能力，以便在需要时启动模拟。这种功能对于让临床医生参与高性能计算至关重要，使他们能够在方便的时候执行模拟并使用资源。我们相信，该项目将提供一个原型脑血流建模环境，这将是相当大的价值，临床医生;我们将继续工作超出本项目的范围，利用这项技术用于日常手术规划。