A Compute Cluster for Brain Imaging and Analysis

用于脑成像和分析的计算集群

• 批准号: 9072820
• 负责人: ALLEN W SONG
• 金额: $ 56.82万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9072820
• 关键词: Big Data; Brain; Brain imaging; Collaborations; Data; Data Analyses; Data Set; Diffusion Magnetic Resonance Imaging; Fiber; Funding; High Performance Computing; Image; Image Analysis; Linux; Magnetic Resonance Imaging; Maps; Memory; Methods; Predisposition; Process; Research; Research Personnel; Research Project Grants; Resolution; Running; Scientist; Series; Speed; United States National Institutes of Health; Universities; cluster computing; computer infrastructure; computing resources; cost; experience; improved; medical schools; meetings; neuroimaging

 DESCRIPTION: Neuroimaging research using high-resolution functional and structural MRI (including diffusion tensor imaging, or DTI) involves the acquisition and analysis of very large data sets that then must be transformed through a series of computation-intensive processing tasks into meaningful information. This project, entitled "A Compute Cluster for Brain Imaging and Analysis", is submitted by NIH-supported investigators at Duke University who primarily conduct neuroimaging research at the Duke - UNC Brain Imaging and Analysis Center at Duke University Medical School. The collaborating scientists have found that the sheer volume of data resulting from improved acquisition and analysis methods has made it increasingly difficult to fully analyze data from all the labs in a timely manner. With our existing computational infrastructure, some processes such as the ultrahigh resolution (submillimeter) diffusion MRI through massive parallel imaging (both in-plane and multi-band through-plane) would take over 20 hrs for a typical ten-run whole-brain functional exam. Also, specific large matrix inversions fo our ultrahigh resolution susceptibility mapping would require memory of up to 700 GB per node. Thus, we are requesting funds to purchase a high- performance compute cluster to meet these and other processing requirements. Our proposal builds upon our past experience administering a 60-node, though quite dated, Linux cluster (Dell PowerEdge 1950), with high-speed fiber channel adaptors and connections to our multiple data servers. We anticipate that this much improved computational resource will greatly enhance our ability to meet the much increased MRI acquisition capability and analysis demand in the many NIH-sponsored research projects, and gain maximum efficiency at lower overall cost. In addition to meeting the computational needs of our neuroimaging scientists, this proposal also benefits from the close collaboration that already exists among us in Duke's big data initiative to have a wider impact.