PARALLEL RECONSTRUCTION SOFTWARE FOR IN VIVO FLUORESCENCE TOMOGRAPHY

用于体内荧光断层扫描的并行重建软件

• 批准号: 8363186
• 负责人: Xiaofeng Steve Zhang
• 金额: $ 0.61万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8363186
• 关键词: Algorithms; Animals; Basic Science; Biological; Communities; Computer software; Computers; Data; Diffusion; Elements; Fluorescence; Funding; Future; Grant; Image; Label; Life; Light; Literature; Methods; Microscopy; Monte Carlo Method; Mus; National Center for Research Resources; Organism; Performance; Principal Investigator; Programming Languages; Research; Research Infrastructure; Resolution; Resources; Sampling; Solutions; Source; Supercomputing; Technology; Time; Tissues; United States National Institutes of Health; Update; Work; base; computerized data processing; computing resources; cost; diffuse optical tomography; image reconstruction; imaging modality; improved; in vivo; light scattering; parallel computing; pre-clinical; reconstruction; research and development; research study; response; simulation; software development; supercomputer; tomography

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. This Technological Research and Development project is also collaborative, and is updated to the following: Fluorescence diffuse optical tomography (FDOT) is a noninvasive method of imaging labeled molecules in living organisms, which could revolutionize both basic research and preclinical imaging. Currently, the primary limitation is resolution, and it is widely accepted that the resolution is fundamentally limited by light scattering in biological tissue. A key to successful reconstruction or high resolution is an accurate forward problem solver for light propagation in highly heterogeneous medium, such a mouse. It has been shown in our latest studies as well as in the literature that analytical solutions and finite element methods based on the diffusion approximation are inadequate for free-space FDOT for small animals. In response, we are going to use Monte Carlo (MC) method to solve the forward problem. However, the complexity in geometry and the large number of sampling points in free-space FDOT experiment demand computing resources far beyond what a regular desktop computer can offer. In this project, we are going to work with our collaborators at Pittsburgh Supercomputing Center PSC), and apply parallel computing technology to our previously developed MC-based software packages for FDOT. Specifically, the aims of this project are: (1) Integrate existing software implemented in C (MC simulation) and MATLAB (data processing and image reconstruction) programming languages. (2) Modify the integrated software so that it can utilize parallel computing facilities (supercomputers) at PSC, in order to significantly improve computing performance (one the order of 1000 in terms of computing time reduction) of image reconstruction. (3) Improve the algorithms and implementation of the MC software, so that the simulation/computation results will be more accurate for high-resolution applications. (4) Apply the parallel reconstruction software to FDOT data acquired previously at our lab as well as in future phantom and animal studies. (5) The developed software will be freely available to the public to benefit the scientific community.

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 子项目的主要研究者可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 该技术研究和开发项目也是合作项目，并更新如下： 荧光扩散光学层析成像（FDOT）是一种非侵入性的活体标记分子成像方法，它可以彻底改变基础研究和临床前成像。目前，主要的限制是分辨率，并且广泛接受的是分辨率从根本上受到生物组织中的光散射的限制。成功重建或高分辨率的关键是一个精确的正问题求解器的光在高度不均匀的介质，如鼠标。它已被证明在我们的最新研究以及在文献中，解析解和有限元方法的基础上的扩散近似是不够的自由空间FDOT的小动物。因此，我们将使用蒙特卡罗（MC）方法来解决正问题。然而，在自由空间FDOT实验的几何形状和大量的采样点的复杂性要求计算资源远远超出了常规的台式计算机可以提供。在这个项目中，我们将与匹兹堡超级计算中心的合作者一起工作，并将并行计算技术应用于我们以前为FDOT开发的基于MC的软件包。具体而言，该项目的目标是： (1)集成现有的C（MC模拟）和MATLAB（数据处理和图像重建）编程语言实现的软件。 (2)修改集成软件，使其能够利用PSC的并行计算设施（超级计算机），以显著提高图像重建的计算性能（计算时间减少1000的数量级）。 (3)改进MC软件的算法和实现，使模拟/计算结果在高分辨率应用中更加准确。 (4)将并行重建软件应用于先前在我们实验室以及未来体模和动物研究中采集的FDOT数据。 (5)开发的软件将免费提供给公众，以造福科学界。