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）：小动物成像几乎可以模拟所有人类疾病，已广泛应用于临床前研究。在此背景下，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优越的灵敏度。特别是，荧光断层扫描（FLT）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，提高 FLT 性能以实现更准确、更可靠的量化仍然很困难。一个关键事实是缺乏准确、快速的体内光传播模型。流行的扩散近似在小动物成像中经常失败，而辐射传递方程（RTE）是最准确的现实模型。多个小组的研究表明，基于 RTE 的重建比基于 DA 的重建具有更好的准确性。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，通常需要几天时间才能完成 3D 小鼠模型的一次重建）。为了与大多数研究基于 RTE 的重建的团队区分开来，我们最近致力于开发 RTE 数值求解器，以提高其实际使用的准确性、效率和灵活性。在这个项目中，我们提出了一种超快的 RTE 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），为此我们将开发新颖的散射自适应计算并通过图形处理单元实现并行化（GPU）。据我们所知，所提出的 RTE 求解器将是同类中的首创。另一方面，通过线性复源公式、光学背景同步校正和各种最先进的重建技术（例如L1促进稀疏性、框架正则化平滑度、Bregman方法和多层次方法。该提案的特点是超快速的 RTE 求解器和创新的重建技术。该提案的总体目标是开发快速、准确且实用的基于 RTE 的 FLT，用于小动物成像。我们受到两个主要独立假设的推动：（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) 当与各种最先进的重建技术相结合时，线性复合源公式和光学背景的同步校正将进一步显着地定量改进 FLT。该项目完成后，所提出的方法将在模型实验中得到验证，并应用于小动物研究。该软件将在网络上公开提供。