Markov Chain Monte Carlo Random Effects Modelling in Diffusion MRI: a New Window on Microstructure and White Matter Architecture

扩散 MRI 中的马尔可夫链蒙特卡罗随机效应建模：微观结构和白质结构的新窗口

• 批准号: EP/G025452/1
• 负责人: Christopher Clark
• 金额: $ 43.69万
• 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: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FG025452%2F1
• 关键词: Markov; Chain; Monte; Carlo; Random

Magnetic resonance imaging (MRI) has revolutionised the way in which we can produce pictures of the brain. MRI can be used to produce pictures of the brain structure by measuring the way in which water can move around in the microscopic structure of the brain tissue.The white matter of the brain consists of long cellular structures that connect different brain regions togther, this can be thought of as the wiring of the brain. Because the water can move more easily along these structures, it is possible to produce images of these connections with a technique called tractography.Tractography is useful in many clinical investigations, most notably in neurosurgical planning. The neurosurgeon aims to remove an abnormal part of the brain whilst leaving these important connections intact. Tractography therefore helps to navigate the neurosurgeon by revealing the location of important pathways with respect to the abnormality to be removed. These techniques can therefore help to improve patient outcomes and reduce post-surgical disability.The challenge however, in applying these techniques is that they have to be used with limited MRI information, because sick patients are unable to tolerate long scans. An MRI scan is digitized and just like an image from a digital camera consists of picture elements or pixels. We refer to these as voxels because the MRI scan has an associated slice thickness.Analysis of MRI scans is usually done on a voxel by voxel basis. This commonly used method therefore treats the signal in each voxel as independant. In this project we will use a method that does not make this assumption but uses information from adjacent or similarly responding voxels. We call this approach information borrowing and achieve this using Bayesian random effects modelling.We aim to apply this approach to improve the accuracy and robustness of tractography thereby providing clinicians with better tools for neurosurgical planning and other clinical investigations. We will also apply these methods in new techniques for measuring the brain microstructure. This latter aim, although ambitious, will provide new markers of tissue structure that are more directly representative of the structure responsible for the functioning of the brain.

磁共振成像 (MRI) 彻底改变了我们生成大脑图像的方式。 MRI 可通过测量水在脑组织微观结构中移动的方式来生成大脑结构的图像。大脑的白质由将不同大脑区域连接在一起的长细胞结构组成，这可以被认为是大脑的线路。由于水可以更容易地沿着这些结构移动，因此可以使用称为纤维束成像的技术生成这些连接的图像。纤维束成像在许多临床研究中很有用，尤其是在神经外科计划中。神经外科医生的目标是切除大脑的异常部分，同时保持这些重要连接完好无损。因此，纤维束成像通过揭示与要去除的异常相关的重要通路的位置来帮助神经外科医生导航。因此，这些技术可以帮助改善患者的治疗效果并减少术后残疾。然而，应用这些技术面临的挑战是，它们必须在有限的 MRI 信息下使用，因为病人无法忍受长时间扫描。 MRI 扫描是数字化的，就像数码相机中的图像由图片元素或像素组成一样。我们将这些称为体素，因为 MRI 扫描具有相关的切片厚度。MRI 扫描的分析通常是在逐个体素的基础上进行的。因此，这种常用的方法将每个体素中的信号视为独立的。在这个项目中，我们将使用一种不做这种假设但使用来自相邻或类似响应体素的信息的方法。我们将这种方法称为信息借用，并使用贝叶斯随机效应建模来实现这一目标。我们的目标是应用这种方法来提高纤维束成像的准确性和鲁棒性，从而为临床医生提供更好的神经外科规划和其他临床研究工具。我们还将把这些方法应用到测量大脑微观结构的新技术中。后一个目标虽然雄心勃勃，但将提供新的组织结构标记，更直接地代表负责大脑功能的结构。

项目成果

Markov Chain Monte Carlo Random Effects Modeling in Magnetic Resonance Image Processing Using the BRugs Interface to WinBUGS

• DOI: 10.18637/jss.v044.i02
• 发表时间: 2011-10
• 期刊: Journal of Statistical Software
• 影响因子: 5.8
• 作者: M. King;F. Calamente;C. Clark;D. Gadian

A Bayesian random effects model for enhancing resolution in diffusion MRI.

用于增强扩散 MRI 分辨率的贝叶斯随机效应模型。

• 作者: Christopher Clark (Author)