论文标题
使用稀疏性和总变化的统计反转，先验和蒙特卡洛抽样方法进行弥漫光学层析成像
Statistical Inversion Using Sparsity and Total Variation Prior And Monte Carlo Sampling Method For Diffuse Optical Tomography
论文作者
论文摘要
在本文中，我们从边界光子密度测量值中确定光学参数，散射和吸收的统计环境中弥漫光学层析成像（DOT）中的重建问题。提出了一种特殊的自适应大都市算法，用于使用稀疏性和总变化的重建程序。最后，对具有不同正则化函数的该技术的模拟研究及其与确定性迭代式正则化的高斯牛顿方法的比较显示了该方法的有效性和稳定性。
In this paper, we formulate the reconstruction problem in diffuse optical tomography (DOT) in a statistical setting for determining the optical parameters, scattering and absorption, from boundary photon density measurements. A special kind of adaptive Metropolis algorithm for the reconstruction procedure using sparsity and total variation prior is presented. Finally, a simulation study of this technique with different regularization functions and its comparison to the deterministic Iteratively Regularized Gauss Newton method shows the effectiveness and stability of the method.
