Copy of A Monte-Carlo diffusion simulation framework for diffusion MRI

用于扩散 MRI 的蒙特卡罗扩散模拟框架的副本

• 批准号: EP/E064280/1
• 负责人: Daniel Alexander
• 金额: $ 50.8万
• 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%2FE064280%2F1
• 关键词: Copy; Monte; Carlo; diffusion; simulation

Diffusion MRI measures the random thermal movement (diffusion) of water molecules within samples. The microstructure of the sample controls the scatter pattern of the particles within. Diffusion MRI allows us to measure this scatter pattern and thus to make inferences about the material microstructure. A major application is neuroimaging, because the brain contains different types of tissue with different microstructure and those microstructures can change during normal development or in disease. Changes in tissue microstructure are one of the earliest signs of disease. Thus diffusion MRI, which is completely non-invasive, has the potential to provide the early-warning systems of the future for degenerative brain diseases, such as dementia and multiple sclerosis. Another application of diffusion MRI within neuroimaging is connectivity mapping. White matter in the brain consists of bundles of axon fibres; it is the electrical cabling that connects different brain regions. Water molecules move further along fibres than across them, because they cannot pass through the fibre walls. From diffusion MRI measurements, we can determine the direction in which particles scatter most. Those directions provide an estimate of the fibre direction at every point in a 3D brain image. Tractography algorithms then reconstruct global fibre trajectories by following fibre-direction estimates from point to point through the image and thus reveal the connectivity of the brain.Only in the last decade has MRI scanner technology reached the point where we can perform diffusion MRI routinely on patients and start to exploit its full potential. The field is young; misconceptions are widespread and the limitations of the technique remain unclear even to the experts. Accurate simulations provide a mechanism for optimizing existing approaches and estimating their accuracy, testing and tuning new applications and exploring the limits of the technique's potential. This project will develop a general purpose simulation tool for diffusion MRI. We will create geometric models of tissue microstructure containing impermeable barriers that restrict water mobility. We can simulate particle diffusion within these models to approximate the measurements we expect from diffusion MRI. The project will also develop image-processing tools to construct geometric tissue models from high magnification microscope images that show the microstructure of brain tissue. Finally, we will demonstrate use of the system by addressing several outstanding questions in diffusion MRI. Specifically, we will use the geometric models and simulations to optimize diffusion MRI measurements, improve the accuracy of connectivity mapping and answer fundamental questions about the mechanisms that contribute to changes in diffusion MRI measurements that we observe during disease and normal brain activation and development.

扩散MRI测量样本内水分子的随机热运动（扩散）。样品的微观结构控制着内部颗粒的散射模式。扩散MRI使我们能够测量这种散射模式，从而对材料的微观结构进行推断。一个主要的应用是神经成像，因为大脑包含不同类型的组织，具有不同的微观结构，这些微观结构可以在正常发育或疾病期间发生变化。组织微结构的变化是疾病的最早迹象之一。因此，完全非侵入性的扩散MRI有可能为痴呆症和多发性硬化症等退行性脑部疾病提供未来的预警系统。扩散MRI在神经成像中的另一个应用是连接映射。大脑中的白色物质由成束的轴突纤维组成;它是连接不同大脑区域的电线。水分子沿纤维沿着移动的距离要远于穿过纤维的距离，因为它们不能穿过纤维壁。通过扩散MRI测量，我们可以确定粒子散射最多的方向。这些方向提供了对3D大脑图像中每个点的纤维方向的估计。纤维束成像算法随后通过在图像中逐点跟踪纤维方向估计来重建全局纤维轨迹，从而揭示大脑的连通性。直到最近十年，MRI扫描仪技术才达到我们可以对患者进行常规弥散MRI的程度，并开始充分利用其潜力。该领域是年轻的;误解是普遍的，该技术的局限性仍然不清楚，即使是专家。精确的模拟提供了一种机制，用于优化现有方法并估计其准确性，测试和调整新应用程序以及探索该技术潜力的极限。该项目将开发一种用于扩散MRI的通用模拟工具。我们将创建组织微观结构的几何模型，其中包含限制水流动性的不可渗透屏障。我们可以在这些模型中模拟粒子扩散，以近似我们期望从扩散MRI获得的测量结果。该项目还将开发图像处理工具，从显示脑组织微观结构的高倍显微镜图像中构建几何组织模型。最后，我们将通过解决弥散MRI中的几个突出问题来演示该系统的使用。具体来说，我们将使用几何模型和模拟来优化弥散MRI测量，提高连接映射的准确性，并回答有关机制的基本问题，这些机制有助于我们在疾病和正常大脑激活和发育期间观察到的弥散MRI测量变化。

项目成果

High-fidelity meshes from tissue samples for diffusion MRI simulations.

用于扩散 MRI 模拟的组织样本的高保真网格。

• DOI: 10.1007/978-3-642-15745-5_50
• 发表时间: 2010
• 期刊: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
• 作者: Panagiotaki E

Two-compartment models of the di usion MR signal in brain white matter

脑白质弥散 MR 信号的两室模型

• 发表时间: 2009
• 作者: Anthony Price