Edmans Andrew, Intes Xavier
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th street, Troy, NY 12180, USA.
Photonics. 2015 Jun;2(2):375-391. doi: 10.3390/photonics2020375. Epub 2015 Apr 9.
Mesh-based Monte Carlo techniques for optical imaging allow for accurate modeling of light propagation in complex biological tissues. Recently, they have been developed within an efficient computational framework to be used as a forward model in optical tomography. However, commonly employed adaptive mesh discretization techniques have not yet been implemented for Monte Carlo based tomography. Herein, we propose a methodology to optimize the mesh discretization and analytically rescale the associated Jacobian based on the characteristics of the forward model. We demonstrate that this method maintains the accuracy of the forward model even in the case of temporal data sets while allowing for significant coarsening or refinement of the mesh.
用于光学成像的基于网格的蒙特卡罗技术能够对光在复杂生物组织中的传播进行精确建模。最近，它们已在一个高效的计算框架内得到发展，用作光学层析成像中的正向模型。然而，常用的自适应网格离散化技术尚未应用于基于蒙特卡罗的层析成像。在此，我们提出一种方法来优化网格离散化，并根据正向模型的特性对相关雅可比矩阵进行解析重缩放。我们证明，即使在处理时间数据集的情况下，该方法也能保持正向模型的准确性，同时允许对网格进行显著的粗化或细化。
