Developing and Optimising MRI Tissue Electrical Conductivity Mapping Methods for Structural and Functional Neuroimaging

1)简要说明研究背景，包括潜在影响MRI在神经退行性疾病的诊断中是不可或缺的。在其流行率和社会经济负担继续上升的同时，人们对这些问题知之甚少。结构和功能磁共振成像(MRI)可以为神经退行性疾病的早期诊断和潜在的治疗干预提供生物标志物。这项研究的目标是优化MRI方法，以同时绘制组织电导率的结构和功能图，因为MRI电特性断层扫描(EPT)可以区分脑肿瘤类型，并有望揭示癫痫和阿尔茨海默病(AD)等神经退行性疾病中脑组织微结构和离子含量的变化。本研究开发的电导率图(CM)技术将允许从一次高效扫描中获得丰富的一套新颖的多模式MRI对比图。这将使基于组织电导率(除了组织磁化率)的新的组合结构和功能生物标记物的开发用于AD和其他疾病的早期诊断。2)目的和目的本项目的目的是开发和优化MRI采集和CM处理方法，以从专门设计的MRI脉冲序列提供同时的结构和功能脑组织电导率图，该图也可用于定量磁化率图(QSM)。具体目标是：-设计和建立具有多个具有组织等效电导率的隔室的体模(MRI测试对象)-开发一种准确的多回波回波平面成像(ME)的CM方法-在模体中采集的数据-为在健康志愿者中采集的ME-EPI数据开发和优化结构化CM技术-开发和测试静息状态功能性CM的图像处理技术-开发和测试功能性CM的生理性噪声去除方法将在模体和健康志愿者中进行MRI采集脉冲序列和CM算法的优化。这名学生将主要在国立神经病学和神经外科医院的3Tesla Prisma MRI系统工作。我们建议的CM技术是基于复杂MRI信号的相位(偏移量)，因此相位时间演变可以用于QSM，而幅度信号(用于常规成像)仍然可用，并且可以用于标准T2*加权成像和标准功能MRI，而不需要额外的扫描时间成本。3)研究方法的新颖性学生将开发针对结构和功能导电性映射进行优化的CM技术。基于EPI的CM是一种新颖的方法。它还没有被优化，也没有被应用于结构性神经成像。目前还没有关于功能性心肌梗死或静息状态功能性心肌梗死的出版物，这将通过作为本研究计划一部分开发的快速MRI采集序列和CM算法来实现。4)与EPSRC的战略和研究领域保持一致本研究与EPSRC的医疗技术主题最紧密地结合在一起，因为它旨在加速医疗应用的研究。与这项研究相关的具体研究领域是医学成像。如果深度学习被开发并用于电导映射，这项研究还可能涉及人工智能技术。5)任何公司或合作者目前没有公司或外部合作者参与研究。

1) Brief description of the context of the research including potential impactMRI is indispensable in the diagnosis of neurodegenerative diseases. These are poorly understood while their prevalence and socio-economic burden continue to rise. Structural and functional Magnetic Resonance Imaging (MRI) can provide biomarkers for early diagnosis and potential therapeutic intervention in neurodegenerative diseases. The vision for this research is to optimise MRI methods for simultaneous structural and functional mapping of tissue electrical conductivity as MRI electrical properties tomography (EPT) can distinguish between brain tumour types and shows promise for revealing changes in brain tissue microstructure and ion content in neurodegenerative diseases such as epilepsy and Alzheimer's disease (AD).The conductivity mapping (CM) techniques developed in this research will allow a rich set of novel, multimodal MRI contrasts to be obtained from a single, efficient scan. This will allow development of new combined structural and functional biomarkers based on tissue conductivity (in addition to tissue magnetic susceptibility) for early diagnosis of AD and other diseases2) Aims and ObjectivesThe aim of this project is to develop and optimise MRI acquisition and CM processing methods to provide simultaneous structural and functional brain tissue conductivity maps from a specially designed MRI pulse sequence that can also be used for quantitative magnetic susceptibility mapping (QSM).The specific objectives are to:- Design and build a phantom (MRI test object) with several compartments with tissue equivalent conductivities- Develop an accurate CM method for multiple echo echo-planar imaging (ME-EPI) data acquired in phantoms - Develop and optimise structural CM techniques for ME-EPI data acquired in healthy volunteers- Develop and test image processing techniques for resting-state functional CM- Develop and test physiological noise removal methods for functional CMThe optimisation of MRI acquisition pulse sequences and CM algorithms will be carried out in both phantoms and healthy volunteers. The student will work primarily at the 3 Tesla Prisma MRI system at the National Hospital for Neurology and Neurosurgery. The CM technique we propose is based on the phase (offset) of the complex MRI signal so the phase time-evolution can be used for QSM and the magnitude signal (used for conventional imaging) is still available and can be utilised for standard T2*-weighted imaging and standard functional MRI with no extra scan time cost.3) Novelty of Research MethodologyThe student will develop CM techniques optimised for both structural and functional conductivity mapping. EPI-based CM is novel. It has not been optimised or applied for structural neuroimaging. There are no publications on functional CM or resting-state functional CM which will be enabled by the rapid MRI acquisition sequences and CM algorithms developed as part of this research programme. 4) Alignment to EPSRC's strategies and research areasThis research is most closely aligned with EPSRC's healthcare technologies theme as it aims to accelerate research to healthcare applications. The specific research area pertaining to this research is Medical Imaging. The research may also involve artificial intelligence technologies if deep learning is developed and employed for conductivity mapping.5) Any companies or collaborators involvedNo companies or external collaborators are currently involved in the research.