MRI Technology for Measurement of Functional and Structural Connectivity in Brain

用于测量大脑功能和结构连接的 MRI 技术

• 批准号: 7952731
• 负责人: Kawin Setsompop
• 金额: $ 9.49万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-05 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7952731
• 关键词: Acceleration; Address; Algorithms; Anisotropy; Area; Brain; Brain imaging; Clinical; Clinical Sciences; Computer software; Consultations; Coupled; Coupling; Data; Development; Development Plans; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Environment; Face; Fiber; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Health; Human; Image; Imaging Techniques; Imaging technology; Individual; Institution; Knowledge; Magnetic Resonance Imaging; Maps; Measurement; Measures; Mentors; Methodology; Methods; Modeling; Neuronal Injury; Neurosciences; Noise; Pathology; Performance; Phase; Physics; Physiologic pulse; Play; Probability; Process; Property; Protocols documentation; Research; Research Project Grants; Resolution; Rest; Sampling; Scanning; Scheme; Sensitivity and Specificity; Series; Signal Transduction; Slice; Speed; Staging; Study Subject; Techniques; Technology; Testing; Three-Dimensional Imaging; Time; Time Study; Training; Translating; Uncertainty; Work; base; bioimaging; career; career development; clinical application; clinically relevant; computerized data processing; data acquisition; design; graduate student; gray matter; hemodynamics; imaging modality; improved; in vivo; interest; meetings; novel; process optimization; programs; public health relevance; reconstruction; research study; respiratory; scaffold; theories; trend; water diffusion; white matter

DESCRIPTION (provided by applicant): Magnetic resonance imaging has demonstrated the potential for non-invasive mapping of the structural and functional connectivity of the human brain in health and disease. The primary methods that have emerged include diffusion imaging and resting-state functional connectivity mapping. Although these methods have validated capabilities for connectivity mapping, they also face technical limitations which constrain their utility. Diffusion imaging is hampered by low sensitivity and the inefficiency of encoding the diffusion data. Similarly, resting-state functional connectivity is limited in temporal resolution by spatial encoding during whole brain connectivity mapping. In this research project, we hypothesize that we can greatly improve the efficiency of the data acquisition schemes in these methods via multi-slice encoding and simultaneous refocusing acquisition. For example, by increasing the number of images slices obtained per acquisition period from 1 slice to up to 6, we both increase the sensitivity of the data acquisition and greatly reduce the imaging time. This development will help advance an entire class of emerging diffusion methodology which probe the water diffusion and thus white matter and grey matter connectivity in increasing detail over the traditional diffusion tensor image. Similarly, it will increase the spatial-temporal resolution and the sensitivity of resting-state functional connectivity mapping. Improving sensitivity and reduce acquisition time will pave way for routine clinical and clinical science applications of these technologies. During the mentored phase of the project, the candidate will draw on his signal processing and optimization theory expertise to design RF pulses and reconstruction algorithms, while gaining knowledge in neuroscience and MR physic to develop acquisition sequences, as well as process and interpret the brain connectivity data. In the later stage, by combining various components of this project, experiments will be carried out to obtain high signal in vivo data in clinically relevant time frame for resting-state functional connectivity mapping and diffusion imaging via DTI, Q-ball, and DSI. The project fits the candidate's long-term career goal of establishing a high-quality independent research program on data acquisition methodology in MRI that will fully utilizes the knowledge and the inter-play between software algorithm development, MR physic, and the underlying neuroscience. The mentored phase will be carried out at the MGH Martinos Center for Biomedical Imaging where the candidate will take advantage of the advanced high-field MRI facility and expertise. Furthermore, the candidate will make use of the world renowned educational opportunities at the Center's affiliated institutions (MIT and Harvard). His career development plan includes training in MR physics and sequence design, diffusion imaging and brain connectomics, consultations with experts and coursework in neuroscience; and participation in seminars and scientific meetings. As part of initiating his own independent research program, the candidate will help mentor a graduate student who will be involved in this project. PUBLIC HEALTH RELEVANCE: Diffusion imaging and resting-state fMRI are potent methods for visualizing pathology and mapping connectivity in the brain. This work will develop highly efficient data acquisition scheme to increase speed and sensitivity of these methods, thus making them more applicable for clinical use.

描述(申请人提供)：磁共振成像已经证明了在健康和疾病中非侵入性绘制人脑结构和功能连接的潜力。出现的主要方法包括扩散成像和静息状态功能连接图。虽然这些方法具有经过验证的连通性映射能力，但它们也面临着限制其实用性的技术限制。扩散成像受到低灵敏度和对扩散数据进行编码的低效的阻碍。同样，静息状态的功能连通性在时间分辨率上受到全脑连通性映射过程中空间编码的限制。在本研究项目中，我们假设可以通过多切片编码和同步重聚焦采集来极大地提高这些方法中的数据采集方案的效率。例如，通过将每个采集周期获取的图像切片数从1个增加到最多6个，我们既提高了数据采集的灵敏度，又大大减少了成像时间。这一发展将有助于推动整个类别的新兴扩散方法的发展，这些方法比传统的扩散张量图像更详细地探测水的扩散，从而更详细地探测白质和灰质的连通性。同样，它将提高静息状态功能连通性映射的时空分辨率和灵敏度。提高灵敏度和减少采集时间将为这些技术的常规临床和临床科学应用铺平道路。在项目的指导阶段，候选人将利用他的信号处理和优化理论专业知识来设计RF脉冲和重建算法，同时获得神经科学和磁共振物理学的知识来开发采集序列，以及处理和解释大脑连接数据。在后期，通过结合本项目的各个组成部分，将进行实验，以获得临床相关时间框架内的高信号活体数据，用于通过DTI、Q-ball和DSI进行静息状态功能连接标测和扩散成像。该项目符合候选人的长期职业目标，即建立一个关于磁共振数据采集方法的高质量独立研究计划，该计划将充分利用软件算法开发、磁共振物理学和基础神经科学之间的知识和相互作用。指导阶段将在MGH Martinos生物医学成像中心进行，候选人将在那里利用先进的高场磁共振设备和专业知识。此外，应聘者将利用该中心附属机构(麻省理工学院和哈佛大学)的世界知名教育机会。他的职业发展计划包括在磁共振物理学和序列设计、扩散成像和脑连接学方面的培训，咨询专家和神经科学方面的课程，以及参加研讨会和科学会议。作为启动自己的独立研究计划的一部分，候选人将帮助指导一名将参与该项目的研究生。 与公共健康相关：扩散成像和静息状态功能磁共振成像是可视化病理和绘制大脑连接图的有效方法。这项工作将开发高效的数据采集方案，以提高这些方法的速度和灵敏度，从而使其更适用于临床。