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: 8507873
• 负责人: Kawin Setsompop
• 金额: $ 24.9万
• 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 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8507873
• 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; reconstruction; research study; respiratory; scaffold; theories; trend; water diffusion; white matter

Project summary: 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.

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