Modelling Complex-Valued Diffusion Tensor Imaging Data and Efficient Methods for Inference

复值扩散张量成像数据建模和有效的推理方法

基本信息

批准号：
EP/E031536/1
负责人：
Sofia Olhede
金额：
$ 13.01万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Fellowship
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%2FE031536%2F1
关键词：
Modelling Complex Valued Diffusion Tensor

项目摘要

The main focus of this project is the modelling and analysis of brain structure using Diffusion Tensor Imaging (DTI) measurements. DTI is an in vivo medical imaging technique, based on Magnetic Resonance Imaging (MRI) technology, that captures the diffusion of water molecules in tissue. The impediment of this diffusion process by nerves enables the characterisation of white matter structure and the measurement of quantitative descriptions of white matter integrity. DTI has identified white matter alterations for a large number of conditions including Alzheimer's disease, Parkinson's disease, schizophrenia, neurological complications of HIV infection, autism, multiple sclerosis etc. The potential of DTI to generate imaging biomarkers for disease progression opens the door to applications in the pharmaceutical industry for drug discovery and development. DTI stands as one of the most important new technologies that will help us to improve our understanding of the complex structure of the brain. DTI data takes the form of a complex-valued quantity but current practice in the analysis of such data involves naively converting the complex-valued data into a real-valued data set. This procedure is especially detrimental in the analysis of very noisy data sets, and may yield very poor analysis methods. To improve estimation both the amplitude and phase of the complex-valued data should be modelled and used. Once the complex-valued data is treated appropriately, estimation may be very much improved upon.By developing better estimation procedures the number of subjects required to find statistically significant changes between treatment groups, may be reduced. The development of more powerful inferential procedures with estimators that have better sensitivity and specificity may aid, for example, in the early identification of a reduction in atrophy using DTI data, and be utilised for routine characterisation of disease progression. Modern signal processing methods will also be used at two different levels, namely locally at a given location (voxel), and regionally to estimate structure across locations. Locally, the usage of multiscale methods will enable modelling the diffusion without artificial constraints in terms directional preference: in observed data brain fibres may cross, kiss and/or fork at a given voxel, and this must be incorporated in the model.Regionally methods will be developed to characterise structural features observed in the brain, for instance by designing fibre dictionaries, i.e. local decompositions that incorporate the characteristics of brain fibres and nerves. These decompositions can be used to estimate the presence of highly specific features, and enable good estimation even at very low signal to noise ratios. Furthermore as structural features are naturally represented in this framework, it is straightforward to test for potential structural degradation in longitudinal measurements, especially relevant for understanding the development of diseases exhibiting degradation such as Alzheimer's.The proposed methods are then the culmination of a research programme, starting from the basic problem of modelling the amplitude and phase of the DTI measurement in a given direction, building a coherent likelihood for the amplitude and phase, modelling the local structure whilst allowing for complicated fibre structure, and finally producing a model for the entire brain structure across voxels, where the latter can be used to answer questions of human physiognomy. The full tool-kit of methods represents a synthesis of state of the art developments in signal processing, statistics and MRI, and will help answer important physiological question of human disease progression.

该项目的主要重点是使用扩散张量成像（DTI）测量进行建模和分析。 DTI是一种基于磁共振成像（MRI）技术的体内医学成像技术，可捕获水分子在组织中的扩散。神经对这种扩散过程的障碍使白质结构的表征以及对白质完整性的定量描述的测量。 DTI已经确定了许多疾病的白质改变，包括阿尔茨海默氏病，帕金森氏病，精神分裂症，艾滋病毒感染的神经系统并发症，自闭症，多发性硬化症等。DTI的潜力为疾病进展的生物标志物的可能性在药物发现和开发药物发现和开发方面开发了大门。 DTI是最重要的新技术之一，它将帮助我们提高对大脑复杂结构的理解。 DTI数据采用复杂值数量的形式，但是在分析此类数据中的当前实践涉及将复杂值数据转换为实现的数据集。在对非常嘈杂的数据集的分析中，此过程尤其有害，并且可能产生非常差的分析方法。为了提高估计，应建模和使用复杂值数据的幅度和相位。一旦适当地处理复杂值数据，就可以大大改善估计。通过制定更好的估计程序，可以减少治疗组之间发现统计学上显着变化所需的受试者的数量。使用具有更好敏感性和特异性的估计量来开发更强大的推论程序，例如，使用DTI数据可以尽早鉴定萎缩的减少，并用于常规表征疾病进展。现代信号处理方法也将在两个不同的级别上使用，即在给定位置（体素），区域性地估算整个位置的结构。在当地，多尺度方法的使用将使在定向偏好的情况下无需人工限制的扩散来建模：在观察到的数据中，脑纤维可能会在给定的素体处交叉，亲吻和/或叉子，并且必须将其纳入模型中。在区域上，将开发出在大脑中观察到的脑部结构的特征，例如，该特征是由脑中的特征来设计的。纤维和神经。这些分解可用于估计高度特异性特征的存在，即使在非常低的信号与噪声比下，也可以实现良好的估计。此外，由于结构特征在该框架中自然表示，因此在纵向测量中测试潜在的结构性降解是很简单的，尤其是与理解出现降级的疾病的发展相关的疾病，例如，所提出的方法是从基本的问题开始的，从而使研究计划的限制和构建阶段的限制，从而启动了阶段，该方法是从基本的问题开始，该阶段是在建立阶段的范围。振幅和相位的可能性，对局部结构进行建模，同时允许复杂的纤维结构，并最终为整个体素的整个大脑结构产生模型，后者可以用来回答人类phology术的问题。方法的完整工具量代表了信号处理，统计和MRI中最先进的发展状态的综合，并将有助于回答人类疾病进展的重要生理问题。