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

扩散磁共振测量样品中水分子的随机热运动(扩散)。样品的微观结构控制着颗粒在其中的散射模式。扩散磁共振成像使我们能够测量这种散射模式，从而对材料的微观结构做出推断。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，具有不同的微结构，这些微结构在正常发育过程中或在疾病中可能会发生变化。组织微结构的变化是疾病的最早迹象之一。因此，扩散磁共振成像是完全非侵入性的，有可能为未来的退行性脑部疾病提供早期预警系统，如痴呆症和多发性硬化症。磁共振扩散成像在神经成像中的另一个应用是连通性映射。大脑中的白质由一束束轴突纤维组成；它是连接大脑不同区域的电缆。水分子沿着纤维移动的距离比穿过纤维的距离更远，因为它们不能穿过纤维壁。从扩散磁共振测量，我们可以确定粒子最分散的方向。这些方向提供了3D大脑图像中每个点的纤维方向的估计。然后，纤维束成像算法通过在图像中逐点跟踪纤维方向估计来重建全球纤维轨迹，从而揭示大脑的连接。直到最近十年，MRI扫描仪技术才达到可以在患者身上常规执行弥散磁共振成像的地步，并开始充分发挥其潜力。这一领域还很年轻；人们普遍存在误解，即使对专家来说，这项技术的局限性也仍然不清楚。精确的模拟提供了一种机制，用于优化现有方法并估计其精度，测试和调整新的应用程序，并探索该技术的潜力极限。本项目将开发一个用于扩散磁共振成像的通用模拟工具。我们将创建组织微结构的几何模型，其中包含限制水流动性的不透水屏障。我们可以在这些模型中模拟粒子扩散，以接近我们从扩散磁共振成像中预期的测量结果。该项目还将开发图像处理工具，从高倍率显微镜图像中构建几何组织模型，显示脑组织的微观结构。最后，我们将通过解决扩散磁共振成像中的几个突出问题来演示该系统的使用。具体地说，我们将使用几何模型和模拟来优化扩散MRI测量，提高连接地图的准确性，并回答有关导致我们在疾病和正常大脑激活和发育期间观察到的扩散MRI测量变化的基本机制的问题。

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.