Optimising MRI Quantitative Susceptibility Mapping Methods for Efficient Structural and Functional Neuroimaging

1.简要说明研究背景，包括潜在的影响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。这项研究的愿景是优化MRI方法，以同时绘制组织磁化率的结构和功能图，因为定量磁化率图(QSM)已显示出神经成像的前景，揭示了帕金森氏症和阿尔茨海默病(AD)等疾病的脑组织成分变化。这项研究开发的快速、高效的综合扫描将是AD患者的理想选择。它有可能提供一套丰富的新的、多模式的MRI对比，以允许开发新的组合结构和功能的生物标记物，用于AD和其他疾病的早期诊断2。目的和目的本项目的目标是优化MRI采集和QSM处理方法，以便在比用于QSM的典型梯度回波MRI脉冲序列更短的时间内提供同时的结构和功能敏感度图。具体目标是：-开发和测试快速MRI脉冲序列，例如回波平面成像(EPI)-实施最先进的加速技术，例如并行成像和同步多层成像，同时最大限度地减少在多个回波时间进行采集的图像伪影。-为新的、快速采集的图像优化QSM处理流水线，潜在地为反问题和基于深度学习的技术纳入新的正则化方法。-开发和定制用于功能性QSM的生理性噪音去除方法将在幻影和健康志愿者中进行序列和算法的优化。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。QSM是基于复杂MRI信号的相位，因此幅度信号(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。研究方法的新颖性学生将开发一种针对结构和功能QSM进行优化的快速EPI序列。他们将从事包含新图像加速技术的新型MRI脉冲序列的开发和测试，以及开发新的和最佳的QSM重建方法，以生成结构和功能敏感度图。为了测试新序列和QSM加工管道的准确性，学生将设计和建造包含已知磁化率材料的新测试模型。与EPSRC的战略和研究领域保持一致本研究最紧密地与EPSRC的医疗技术主题保持一致，因为它旨在加速医疗应用的研究。与本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应用于QSM重建，该研究还可能涉及人工智能技术。任何公司或合作者目前没有公司或外部合作者参与这项研究。

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 magnetic susceptibility as quantitative susceptibility mapping (QSM) has shown promise for neuroimaging, revealing changes in brain tissue composition in diseases such as Parkinson's and Alzheimer's disease (AD). The rapid, efficient integrated scan developed in this research will be ideal for AD patients. It has the potential to provide a rich set of novel, multimodal MRI contrasts to allow development of new combined structural and functional biomarkers for early diagnosis of AD and other diseases2. Aims and ObjectivesThe aim of this project is to optimise MRI acquisition and QSM processing methods to provide simultaneous structural and functional susceptibility maps in a much shorter time than typical gradient-echo MRI pulse sequences used for QSM.The specific objectives are to:- Develop and test rapid MRI pulse sequences such as echo-planar imaging (EPI)- Implement state-of-the-art acceleration techniques such as parallel imaging and simultaneous multislice imaging while minimising image artifacts for acquisitions at multiple echo times.- Optimise QSM processing pipelines for the new, rapidly acquired images, potentially incorporating new regularisation methods for this inverse problem and deep-learning based techniques. - Develop and tailor physiological noise removal methods for functional QSMThe optimisation of sequences and 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. QSM is based on the phase of the complex MRI signal so 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 a rapid EPI sequence optimised for both structural and functional QSM.They will engage in development and testing of novel MRI pulse sequences incorporating new image acceleration techniques as well as developing novel and optimal methods for QSM reconstruction of the resulting images to produce structural and functional susceptibility maps. To test the accuracy of new sequences and QSM processing pipelines, the student will design and build new test phantoms containing materials with known magnetic susceptibilities.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 QSM reconstruction.5. Any companies or collaborators involvedNo companies or external collaborators are currently involved in the research.