A Linux Cluster Computational Facility for Neuroimaging Research

用于神经影像研究的 Linux 集群计算工具

• 批准号: 7595617
• 负责人: ALLEN W SONG
• 金额: $ 26.7万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7595617
• 关键词: Brain; Brain imaging; Collaborations; Computer software; Data; Data Analyses; Data Set; Development; Fiber; Functional Magnetic Resonance Imaging; Funding; Image; Image Analysis; Linux; Magnetic Resonance Imaging; Methods; National Center for Research Resources; North Carolina; Performance; Pilot Projects; Procedures; Process; Productivity; Research; Research Personnel; Research Project Grants; Resolution; Running; Scientist; Series; Speed; Surface; Time; Universities; computer infrastructure; computing resources; cost; experience; image reconstruction; imaging Segmentation; improved; instrument; instrumentation; medical schools; meetings; multicore processor; network models; neuroimaging; public health relevance; reconstruction; supercomputer

DESCRIPTION (provided by applicant): Neuroimaging research using parallel acquired functional MRI and high-resolution structural MRI, and dense array electrical recording involves the acquisition 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 Linux Cluster Computational Facility for Neuroimaging Research", is jointly submitted by NIH-supported investigators at Duke University and the University of North Carolina 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 methods and improved hardware 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 iterative reconstruction of time-course spiral images acquired through eight to sixteen parallel receiver channels would take over 20 hrs for a typical ten-run functional exam, and a typical brain image segmentation and flattening procedure would take more than 24 hrs. Thus, we are requesting funds to purchase a high-performance Linux cluster to meet these and other processing requirements at a relatively low cost. Our application builds upon our past experience administering a multiprocessor, though quite dated, supercomputer (IBM p670 supported by NCRR in 2002) and our recent pilot project using an eight-node Linux cluster (Dell Power Edge 1950, supported by Duke internal funds), with high-speed fiber channel adaptors and connections to our multiple data servers totaling over 40TB online storage. 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 a low overall cost. In addition to meeting the scientific needs of our neuroimaging scientists, this application also benefits from the close collaboration that already exists among us in the development of analytical software and in the management of large research instruments. PUBLIC HEALTH RELEVANCE: We propose in this S10 application to purchase a 50-node Linux cluster to meet the ever-increasing computational demands from our NIH-funded neuroimaging investigators, as the results of much improved MR imaging hardware and analysis software in the past five years. Because most of our projects share common pre- and post-processing steps such as parallel image reconstructions for fast spiral acquisitions, brain surface flattening and dynamic causal network modeling, this shared instrumentation will greatly improve the productivity and efficiency of our research projects. We anticipate that this cluster will help us gain maximum computational power at the lowest cost taking into consideration the large amount of projects it will support.

描述(申请人提供)：使用平行采集的功能磁共振成像和高分辨率结构磁共振成像的神经成像研究，以及密集阵列电子记录涉及获取非常大的数据集，然后必须通过一系列计算密集型处理任务将其转换为有意义的信息。该项目名为“用于神经成像研究的Linux集群计算设施”，由杜克大学和北卡罗来纳大学的NIH支持的研究人员联合提交，他们主要在杜克大学医学院的杜克-北卡罗来纳大学脑成像和分析中心进行神经成像研究。合作的科学家们发现，改进的采集方法和改进的硬件带来的海量数据，使得及时全面分析所有实验室的数据变得越来越困难。在我们现有的计算基础设施下，一些过程，如迭代重建通过8到16个并行接收器通道采集的时间过程螺旋图像，对于典型的10次功能检查需要20多个小时，而典型的脑图像分割和平面化过程将需要24小时以上。因此，我们正在申请资金购买一个高性能的Linux集群，以相对较低的成本满足这些和其他处理需求。我们的应用程序建立在我们过去管理多处理器超级计算机(IBM p670在2002年得到NCRR支持)和我们最近的试点项目的经验基础上，该群集使用8节点Linux群集(Dell Power Edge 1950，由Duke内部资金支持)，具有高速光纤通道适配器，并连接到我们的多个数据服务器，总计超过40TB的在线存储。我们预计，这种大大改进的计算资源将极大地增强我们的能力，以满足NIH赞助的许多研究项目中大幅增加的MRI采集能力和分析需求，并以较低的总体成本获得最大效率。除了满足我们神经成像科学家的科学需求外，这一应用还得益于我们在分析软件开发和大型研究仪器管理方面已经存在的密切合作。 与公共健康相关：我们在S10应用程序中建议购买50节点的Linux集群，以满足我们由NIH资助的神经成像研究人员不断增长的计算需求，这是过去五年大幅改进的磁共振成像硬件和分析软件的结果。由于我们的大多数项目共享共同的前后处理步骤，例如用于快速螺旋采集的并行图像重建、脑表面平面化和动态因果网络建模，这种共享的仪器将极大地提高我们研究项目的生产力和效率。我们预计，考虑到它将支持的大量项目，该集群将帮助我们以最低的成本获得最大的计算能力。

项目成果

No association of ZNF804A rs1344706 with white matter integrity in schizophrenia: a tract-based spatial statistics study.

ZNF804A rs1344706 与精神分裂症白质完整性无关：基于束的空间统计研究

• DOI: 10.1016/j.neulet.2012.10.062
• 发表时间: 2013-01-04
• 期刊: Neuroscience letters
• 影响因子: 2.5
• 作者: Wei Q;Kang Z;Diao F;Guidon A;Wu X;Zheng L;Li L;Guo X;Hu M;Zhang J;Liu C;Zhao J
• 通讯作者: Zhao J