A Compute Cluster for Brain Imaging and Analysis

 产品说明：使用高分辨率功能和结构MRI（包括弥散张量成像或DTI）的神经成像研究涉及到非常大的数据集的采集和分析，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“用于脑成像和分析的计算集群”，由杜克大学NIH支持的研究人员提交，他们主要在杜克大学医学院的杜克-杜克脑成像和分析中心进行神经成像研究。合作的科学家发现，由于改进的采集和分析方法而产生的大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。利用我们现有的计算基础设施，一些过程，如通过大规模并行成像（平面内和多波段通过平面）的10分辨率（亚毫米）扩散MRI，对于典型的10次全脑功能检查将需要超过20小时。此外，我们的10分辨率磁化率映射的特定大型矩阵求逆将需要每个节点高达700 GB的内存。因此，我们正在申请资金购买一个高性能计算集群，以满足这些和其他处理要求。我们的建议书是基于我们过去管理60节点（虽然相当过时）Linux群集（Dell PowerEdge 1950）的经验，该群集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。我们预计，这种大大改进的计算资源将大大提高我们的能力，以满足越来越多的MRI采集能力和分析需求，在许多NIH赞助的研究项目，并获得最大的效率，在较低的总成本。除了满足我们神经影像科学家的计算需求外，这项提议还受益于我们之间在杜克大数据倡议中已经存在的密切合作，以产生更广泛的影响。

 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.