MONTE CARLO MODELING OF DIFFUSE LIGHT TRANSPORT

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 新的蒙特卡罗技术正在被开发来定量地确定由非侵入性漫射成像方式采样的组织体积。最近的研究活动随着一种新的输运理论方法的发展而达到顶峰，该方法用于成像和分析探测器的条件反应，条件是通过任何指定的组织亚体积进行研究。新的程序依赖于辐射输运的广义互易理论，该理论使计算能够使用两个蒙特卡罗模拟有效地执行：一个跟踪来自源的光子，第二个跟踪在探测器启动的反向移动的光子。这种“中途方法”然后将向前和向后移动的光子配对在目标体积表面匹配的空间角箱中。在目标边界表面上的积分产生访问目标体积和被检测的期望联合概率。该方法已经在双层上皮组织模型上进行了测试，模拟得到的数据被用来比较竞争探针设计的相对优点和效率。然后通过反问题的解来确认这些偏好，该反问题指示了给定源-探测器-靶体积配置的最佳探头设计。未来的工作将包括增加减少差异的战略以及额外的测试和模型验证研究。这种有条件的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针的设计。

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. 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.