Larusson Fridrik, Fantini Sergio, Miller Eric L
Biomed Opt Express. 2011 Mar 25;2(4):946-65. doi: 10.1364/BOE.2.000946.
We explore the development and performance of algorithms for hyperspectral diffuse optical tomography (DOT) for which data from hundreds of wavelengths are collected and used to determine the concentration distribution of chromophores in the medium under investigation. An efficient method is detailed for forming the images using iterative algorithms applied to a linearized Born approximation model assuming the scattering coefficient is spatially constant and known. The L-surface framework is employed to select optimal regularization parameters for the inverse problem. We report image reconstructions using 126 wavelengths with estimation error in simulations as low as 0.05 and mean square error of experimental data of 0.18 and 0.29 for ink and dye concentrations, respectively, an improvement over reconstructions using fewer specifically chosen wavelengths.
我们探索了用于高光谱漫射光学层析成像（DOT）的算法的开发与性能，该技术可收集数百个波长的数据，并用于确定所研究介质中发色团的浓度分布。详细介绍了一种有效方法，该方法使用应用于线性化玻恩近似模型的迭代算法来形成图像，假设散射系数在空间上是恒定且已知的。采用L曲面框架为反问题选择最佳正则化参数。我们报告了使用126个波长的图像重建结果，模拟中的估计误差低至0.05，墨水和染料浓度实验数据的均方误差分别为0.18和0.29，相较于使用较少特定选择波长的重建结果有了改进。
