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

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: 8191901
• 负责人: Hao Gao
• 金额: $ 16.41万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 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 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8191901
• 关键词: Algorithms; Animals; Arts; Attention; Award; Biology; Chemistry; Code; Complex; Computer Architectures; Computer software; Contrast Media; Development; Diffusion; Drug Formulations; Equation; Evaluation; Fluorescence; Goals; Green Fluorescent Proteins; Hour; Human; Image; Imaging Device; Internet; Label; Light; Literature; Magnetic Resonance Imaging; Malignant Neoplasms; Medicine; Methods; Modeling; Molecular; Mus; Nobel Prize; Optics; Organ; Performance; Play; Process; Recovery; Research; Role; Scientist; Site; Source; Speed; Structure; Study models; Techniques; Testing; Time; United States National Institutes of Health; Work; X-Ray Computed Tomography; base; biological systems; cancer cell; flexibility; fluorescence imaging; human disease; image reconstruction; improved; in vivo; in vivo Model; innovation; interest; mathematical theory; molecular imaging; mouse model; novel; optical imaging; pre-clinical; pre-clinical research; prevent; reconstruction; research study; theories; tomography; tumor; web site; working group

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. PUBLIC HEALTH RELEVANCE: In this project, we will use the most recent development in mathematical theory and computer architecture to improve fluorescence tomography with applications in small animal imaging for human cancer studies. An ultra-fast solver will be developed for the most accurate model - radiative transfer equation, and the state-of-art reconstruction techniques will be incorporated to significantly improve both accuracy and efficiency.

描述（由申请人提供）：小动物成像技术被广泛应用于临床前研究，它可以模拟几乎所有的人类疾病。在此背景下，近年来光学成像备受关注，其中荧光成像由于外源性造影剂的作用，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性能以获得更准确和可靠的量化仍然是困难的。一个关键的事实是缺乏一个准确和快速的模型，在体内的光传播。常用的扩散近似法在小动物成像中常常失效，而辐射传递方程（RTE）是最准确的现实模型。几个小组已经表明，基于rte的重建比基于da的重建提供了明显更好的准确性。然而，阻碍RTE普及的主要问题是其不切实际的计算负担（例如，在3D鼠标模型上完成一次重建通常需要几天时间）。为了区别于大多数基于RTE重建的团队，我们最近致力于RTE数值求解器的开发，以提高其在实际应用中的准确性、效率和灵活性。在这个项目中，我们提出了一个超快速的RTE求解器，使基于RTE的FLT可行（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，提出的RTE求解器将是同类中的第一个。另一方面，通过线性复源公式、光学背景同步校正和各种先进的重建技术（如l1提升稀疏性、帧正则化平滑、Bregman方法和多层方法）的协同组合，可以进一步提高FLT。该方案的特点是超快速求解RTE和创新的重建技术。本课题的总体目标是开发快速、准确、实用的基于rte的小动物成像FLT。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使RTE的超快速求解成为可能，这使得基于RTE的FLT成为可能；(2)当结合各种初始重建技术时，线性复源公式和光学背景的同步校正将进一步显著提高FLT的定量。本项目完成后，建议的方法将在模拟实验中得到验证，并应用于小动物研究。该软件将在网上公开。