TRANSPORT COMPUTATIONAL METHODS

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 而子项目的主要调查员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。 本研究的总体目标是：a）改进处理光学探针获得的数据所需的计算方法的设计和应用，以及B）扩展我们在亚毫米至厘米长度尺度上分析和成像异质组织结构的能力。 目前用于对光在组织中的传输（正问题）和确定异质组织结构的光学性质（逆问题）进行建模的绝大多数方法严重依赖于标准或增强的光学扩散模型，偶尔使用详细的蒙特卡罗模拟作为补充。 我们正在开发先进的蒙特卡罗方法，导致更快，更准确，在某些情况下，详细的组织/光传输问题的全球解决方案。新的方法建模的近似解决方案的正问题和反问题也已开发。 努力集中在选择性地应用这些方法，直接相关的LAMMP核心程序技术和相关的合作研究的正问题和逆问题。 在其他地方报告的是最近的进展“微扰蒙特卡罗技术的扩散光子传输”，“蒙特卡罗模拟扩散光子迁移”，和“凝聚历史蒙特卡罗算法”。在这里，我们报告了自适应蒙特卡罗算法（即，算法能够学习，实现几何收敛的一般运输问题。我们已经发现了一种新的几何收敛的方法，用于估计在任意分解的组织相空间的光的详细的空间角度分布的平均值。这项新技术具有很大的潜力，使精确的辐射传输计算方法更常规的使用生物医学和其他应用在生物学和医学。

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. The overall goals of this proposed research are to a) improve the design and application of computational methods necessary to process data obtained by optical probes and b) extend our capacity to analyze and image heterogeneous tissue structures on sub-millimeter to centimeter length scales. The vast majority of methodologies currently used to model the transport of light in tissue (the forward problem) and to determine the optical properties of heterogeneous tissue structures (the inverse problem) rely heavily on standard or enhanced optical diffusion models, supplemented by the occasional use of detailed Monte Carlo simulations. We are developing advanced Monte Carlo methods that lead to faster, more accurate and, in some instances, detailed global solutions of tissue/light transport problems. Novel methods for modeling approximate solutions of both forward and inverse problems have also been developed. Effort has concentrated on the selective application of these methods to forward and inverse problems of direct relevance to LAMMP core program technologies and to related collaborative research. Reported elsewhere is recent progress on "Perturbation Monte Carlo Techniques for Diffuse Photon Transport", "Monte Carlo Modeling for Diffuse Photon Migration", and "Condensed History Monte Carlo Algorithms". Here we report on a recent breakthrough in the development of adaptive Monte Carlo algorithms (i.e., algorithms capable of learning that achieve geometric convergence for general transport problems. We have discovered a new geometrically convergent method for estimating the averages of the detailed space-angle distribution of light over arbitrary decompositions of the tissue phase space. This new technique has great potential for bringing accurate radiative transport computational methods into more routine use for biomedical and other applications in biology and medicine.