MONTE CARLO MODELING OF DIFFUSE LIGHT TRANSPORT

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。 新的蒙特卡罗技术正在开发中，以定量地确定非侵入性扩散成像模式采样的组织体积。 最近的研究活动已经达到高潮的一个新的运输理论方法的发展，成像和分析的条件响应的检测器，通过任何指定的组织子体积为调查目标的通道条件。新的程序依赖于辐射传输的广义互易理论，该理论使计算能够使用一对蒙特卡罗模拟有效地进行：一个跟踪来自源的光子，第二个跟踪在检测器处发起的向后移动的光子。然后，该“中途方法”在目标体积的表面处的匹配的空间-角度仓中配对向前和向后移动的光子。在目标边界表面上的积分产生访问目标体积和被检测到的期望联合概率。 该方法已被测试的两层上皮组织模型和来自模拟的数据被用来比较竞争探针设计的相对优点和效率。这些偏好，然后通过逆问题的解决方案，表明最佳的探头设计为一个给定的源-检测器-目标体积配置确认。未来的工作将包括增加减少差异的策略和额外的测试和模型验证研究。这种条件检测器响应信息的使用应极大地有助于为特定应用定制的新型探头的设计。

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. Novel Monte Carlo techniques are being developed to quantitatively determine the tissue volume sampled by non-invasive diffuse imaging modalities. Recent research activity has culminated with the development of a new transport-theoretic method for imaging and analyzing the conditional response of a detector, conditioned by passage through any designated tissue subvolume targeted for investigation. The new procedure relies on a generalized reciprocity theory for radiative transport that enables the computation to be performed efficiently using a pair of Monte Carlo simulations: one tracking photons from the source, and the second tracking backward-moving photons initiated at the detector. This "midway method" then pairs the forward and backward -moving photons in matched spatial-angular bins at the surface of the targeted volume. An integration over the target bounding surfaces produces the desired joint probability of both visiting the targeted volume and being detected. The method has been tested on a two-layer epithelial tissue model and the data derived from the simulations is used to compare the relative merits and efficiencies of competing probe designs. These preferences are then confirmed through the solution of inverse problems that indicate best probe designs for a given source-detector-target volume configuration. Future work will include the addition of variance reduction strategies and additional testing and model validation studies. The use of this conditional detector response information should aid greatly in the design of novel probes customized for a particular application.