TRANSPORT COMPUTATIONAL METHODS

运输计算方法

• 批准号: 7722504
• 负责人: Bruce J. Tromberg
• 金额: $ 0.21万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-15 至 2009-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7722504
• 关键词: Algorithms; Biology; Computer Retrieval of Information on Scientific Projects Database; Computing Methodologies; Development; Diffuse; Diffusion; Funding; Goals; Grant; Image Analysis; Institution; Lead; Learning; Length; Light; Medicine; Methodology; Methods; Modeling; Monte Carlo Method; Optics; Phase; Photons; Property; Recording of previous events; Reporting; Research; Research Personnel; Resources; Solutions; Source; Standards of Weights and Measures; Structure; Techniques; Technology; Tissues; United States National Institutes of Health; computerized data processing; design; improved; migration; millimeter; novel; programs; simulation

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 这项拟议的研究的总体目标是a）改善通过光学探针获得所需的计算方法的设计和应用，b）扩大了我们在亚毫米计上分析和图像异质组织结构的能力，以达到百分之千的长度尺度。 当前用于建模组织中光（正向问题）的运输的绝大多数方法（正向问题），并确定异质组织结构的光学特性（反问题）在很大程度上取决于标准或增强的光学扩散模型，并偶尔使用详细的Monte Carlo Simulations补充。 我们正在开发先进的蒙特卡洛方法，从而导致更快，更准确，并且在某些情况下是详细的组织/光传输问题的全球解决方案。还已经开发了建模前进和反问题的近似解决方案的新方法。 努力集中在这些方法的选择性应用与LAMMP核心计划技术和相关协作研究直接相关的转发和反面问题。 在其他地方报道的是，最近在“扰动蒙特卡洛技术的摄动光子传输技术”，“用于弥漫性光子迁移的蒙特卡洛建模”和“浓缩历史历史蒙特卡洛算法”上的进展。在这里，我们报告了自适应蒙特卡洛算法发展的最新突破（即，能够学习能够实现一般运输问题的几何收敛的算法。我们发现了一种新的收敛方法，用于估计详细的空间范围的详细方法，使得势能分配的详细范围的整合范围，使得新型技术的范围更加有效。常规用于生物医学和其他应用于生物学和医学的应用。