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

基本信息

批准号：
8122200
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
项目类别：
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/8122200
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

项目摘要

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（由申请人提供）：磁共振成像证明了对健康和疾病中人脑的结构和功能连通性无创映射的潜力。出现的主要方法包括扩散成像和静止状态功能连接映射。尽管这些方法已验证了连接映射的功能，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编码扩散数据的效率低下的阻碍。同样，通过在整个大脑连接映射过程中进行空间编码，静息状态功能连接在时间分辨率中受到限制。在该研究项目中，我们假设我们可以通过多层编码和同时重新聚焦获取来大大提高这些方法中数据采集方案的效率。例如，通过将每个采集周期获得的图像切片数量从1片增加到6个，我们都提高了数据采集的敏感性，并大大减少了成像时间。这种发展将有助于推进整个新兴的扩散方法，这些方法探测了水扩散，因此，与传统扩散张量图像相比，细节越来越多。同样，它将增加空间分辨率和静止状态功能连接映射的灵敏度。提高灵敏度并减少获取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，同时在神经科学和MR Physic方面获得知识来开发获取序列，以及处理和解释大脑连接性数据。在后期，通过结合该项目的各种组件，将进行实验以在临床相关的时间范围内获得高信号，以通过DTI，Q-BALL和DSI静止相关的时间范围，以静止相关的时间范围。该项目符合候选人的长期职业目标，即在MRI中建立高质量的独立研究计划，该计划将充分利用软件算法开发，MR Physic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，候选人将利用高级高场MRI设施和专业知识。此外，候选人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MR物理学和序列设计，扩散成像和脑连接组学，与专家的磋商以及神经科学课程的培训；并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，候选人将帮助指导将参与该项目的研究生。公共卫生相关性：扩散成像和静止状态fMRI是可视化病理学和绘制大脑连通性的有效方法。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，从而使它们更适用于临床使用。