Fluorescence Tomography in Small Animal Imaging using an Ultra-fast RTE S olver

使用超快 RTE 解算器进行小动物成像中的荧光断层扫描

• 批准号: 8605736
• 负责人: Hao Gao
• 金额: $ 10.42万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-15 至 2013-11-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8605736
• 关键词: Fluorescence; Tomography; Small; Animal; Imaging

DESCRIPTION (provided by applicant): Small animal imaging, which models almost all human diseases, has been widely used in preclinical research. In this context, optical imaging has attracted great attention over past several years, among which fluorescence imaging has the superior sensitivity due to exogenous contrast agent. In particular, fluorescence tomography (FLT) enables the three-dimensional (3D) quantitative recovery of fluorescent source in a non-invasive and non-radiative manner. However, it remains difficult to improve the FLT performance for more accurate and reliable quantification. One crucial fact is the missing of an accurate and fast model for in vivo light propagation. The popular diffusion approximation often fails in small animal imaging, while radiative transfer equation (RTE) is the most accurate of realistic models. It has shown by several groups that RTE-based reconstructions offer significantly better accuracy than DA-based ones. However, the major issue preventing RTE from being popular is its unpractical computational burden (e.g., it usually takes days to complete one reconstruction on 3D mouse model). To distinguish from most groups working on RTE-based reconstructions, we have recently dedicated in the development of numerical solver of RTE to improve its accuracy, efficiency and flexibility for practical use. In this project, we propose an ultra-fast solver of RTE so that RTE-based FLT is feasible (e.g., one reconstruction takes <2 hours), for which we will develop novel scattering-adaptive computation and implement the parallelization via graphics processing unit (GPU). The proposed solver of RTE will be first-of-its-kind to the best of our knowledge. On the other hand, FLT can be further improved by synergetic combination of linear complex-source formulation, simultaneous correction of optical background and various state-of-art reconstruction techniques, such as L1-promoted sparsity, framelet-regularized smoothness, Bregman method and multilevel approach. This proposal is featured by both an ultra-fast solver of RTE and innovative reconstruction techniques. The overall goal of this proposal is to develop fast, accurate and practical RTE-based FLT for small animal imaging. We are motivated by two main independent hypotheses that (1) GPU parallelization and scattering-adaptive computation will allow the ultra-fast solver of RTE, which makes RTE-based FLT feasible; (2) linear complex-source formulation and simultaneous correction of optical background will allow further significant quantitative improvement of FLT when combined with various start-of-art reconstruction techniques. Upon completion of this project, the proposed methods will have been validated in phantom experiments and applied to small animal studies. The software will be made publicly available on web.

描述（由申请人提供）：小动物成像模拟几乎所有人类疾病，已广泛用于临床前研究。在这种背景下，光学成像在过去几年中引起了极大的关注，其中荧光成像由于外源性造影剂而具有上级灵敏度。特别地，荧光断层扫描（FLT）使得能够以非侵入性和非辐射的方式对荧光源进行三维（3D）定量恢复。然而，它仍然难以提高FLT性能更准确和可靠的量化。一个关键的事实是缺乏一个准确和快速的模型在体内光传播。常用的扩散近似法在小动物成像中常常失效，而辐射传输方程（RTE）是最精确的真实模型。几个研究小组已经表明，基于RTE的重建比基于DA的重建提供了更好的准确性。然而，阻碍RTE流行的主要问题是其不切实际的计算负担（例如，在3D小鼠模型上完成一次重建通常需要数天）。为了区别于大多数基于RTE的重建工作组，我们最近致力于RTE的数值求解器的开发，以提高其精度，效率和灵活性，以供实际使用。在这个项目中，我们提出了一个RTE的超快速求解器，使得基于RTE的FLT是可行的（例如，一次重建需要<2小时），为此我们将开发新的散射自适应计算并通过图形处理单元（GPU）实现并行化。据我们所知，RTE的拟议求解器将是同类中的第一个。另一方面，FLT可以进一步提高协同组合的线性复源制定，同时校正的光学背景和各种国家的最先进的重建技术，如L1促进稀疏，框架正则化平滑，布雷格曼方法和多级方法。该建议的特点是一个超快速求解器的RTE和创新的重建技术。该提案的总体目标是开发快速，准确和实用的基于RTE的FLT用于小动物成像。我们的动机是两个主要的独立假设，（1）GPU并行化和散射自适应计算将允许RTE的超快速求解器，这使得基于RTE的FLT可行;（2）线性复源公式和光学背景的同时校正将允许FLT与各种最新的重建技术相结合时，进一步显着的定量改进。本项目完成后，所提出的方法将在体模实验中得到验证，并应用于小动物研究。该软件将在网上公开提供。