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

描述(申请人提供)：小动物成像技术可以模拟几乎所有的人类疾病，已被广泛应用于临床前研究。在这种背景下，光学成像在过去的几年里引起了人们的极大关注，其中荧光成像由于外源造影剂的作用而具有更高的灵敏度。特别是，荧光断层成像(Flt)能够以非侵入性和无辐射的方式对荧光源进行三维(3D)定量恢复。然而，为了获得更准确和可靠的量化，仍然很难提高FLT的性能。一个关键的事实是缺少一个准确而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。几个小组已经表明，基于RTE的重建比基于DA的重建提供了显著更好的准确性。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)。为了区别于大多数致力于基于RTE的重建的小组，我们最近致力于RTE数值求解器的开发，以提高其精度、效率和实际使用的灵活性。在这个项目中，我们提出了一个基于RTE的超快速解算器，使得基于RTE的FLT是可行的(例如，一次重建需要&lt；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，拟议的RTE解算器将是此类解决方案中的第一个。另一方面，通过线性复源公式、光学背景的同时校正和各种最新的重建技术的协同结合，如L1提升的稀疏性、Framelet正则化的光滑化、Bregman方法和多水平方法，可以进一步改进FLT。该方案的特点是RTE的超快速解算器和创新的重建技术。这项建议的总体目标是开发快速、准确和实用的基于RTE的小动物成像的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允许RTE的超快求解器，这使得基于RTE的FLT可行；(2)线性复源公式和同时校正光学背景将允许在结合各种最新的重建技术时进一步显著提高FLT的量化。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。 公共卫生相关性：在这个项目中，我们将使用数学理论和计算机体系结构的最新发展来改进荧光断层扫描，并将其应用于人类癌症研究的小动物成像中。将为最精确的模型--辐射传递方程开发一个超快速求解器，并将采用最先进的重建技术，以显著提高精度和效率。

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.