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

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:
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
项目状态:
已结题
起止时间:
2009 至 --

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中文摘要
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英文摘要
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.
期刊论文(3)
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科研奖励(0)
会议论文
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]
通讯作者: 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。
DOI: --
发表时间:
期刊:
影响因子: --
作者: [Christopher Clark (Author)]
通讯作者: Christopher Clark (Author)
IRES Track 1: RUI: Monitoring of Marine Life Coastal Habitats via Autonomous Robot Systems
  • 批准号:
    1952616
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2020
  • 负责人:
    Christopher Clark
  • 依托单位:
Collaborative Research: Admixture mapping of a hybrid zone to test Tinbergen's emancipation hypothesis
  • 批准号:
    1656867
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $38.4万
  • 财政年份:
    2017
  • 负责人:
    Christopher Clark
  • 依托单位:
IRES: Intelligent Search and Mapping of Submerged Cultural Heritage Ancient Shipwrecks using Autonomous Underwater Vehicles
  • 批准号:
    1460153
  • 项目类别:
    Standard Grant
  • 资助金额:
    $25.0万
  • 财政年份:
    2015
  • 负责人:
    Christopher Clark
  • 依托单位:
RI: Small: RUI: Multi-Robot Systems for Tracking, Monitoring, and Modeling of Periodic Migratory Populations
  • 批准号:
    1423620
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $44.95万
  • 财政年份:
    2014
  • 负责人:
    Christopher Clark
  • 依托单位:
国内基金
海外基金
Supply Chain Collaboration in addressing Grand Challenges: Socio-Technical Perspective
  • 批准号:
    --
  • 项目类别:
    外国青年学者研究基金项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    Lim Jia Jia
  • 依托单位:
在大数据和复杂模型背景下探究更有效的Markov chain Monte Carlo算法
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2022
  • 负责人:
    焦熙云
  • 依托单位:
构建互穿网络结构中系带分子(tie chain)和缠结网络协同提升全聚合物太阳能电池力学与光伏性能
基于Service Chain的数据中心网络资源调度问题研究
  • 批准号:
    61772235
  • 项目类别:
    面上项目
  • 资助金额:
    59.0万元
  • 批准年份:
    2017
  • 负责人:
    崔林
  • 依托单位: