University College London, Department of Computer Science, Gower Street, London WC1E 6BT, United Kingdom.
J Biomed Opt. 2013 Aug;18(8):86008. doi: 10.1117/1.JBO.18.8.086008.
Current fluorescence diffuse optical tomography (fDOT) systems can provide large data sets and, in addition, the unknown parameters to be estimated are so numerous that the sensitivity matrix is too large to store. Alternatively, iterative methods can be used, but they can be extremely slow at converging when dealing with large matrices. A few approaches suitable for the reconstruction of images from very large data sets have been developed. However, they either require explicit construction of the sensitivity matrix, suffer from slow computation times, or can only be applied to restricted geometries. We introduce a method for fast reconstruction in fDOT with large data and solution spaces, which preserves the resolution of the forward operator whilst compressing its representation. The method does not require construction of the full matrix, and thus allows storage and direct inversion of the explicitly constructed compressed system matrix. The method is tested using simulated and experimental data. Results show that the fDOT image reconstruction problem can be effectively compressed without significant loss of information and with the added advantage of reducing image noise.
当前的荧光漫射光学断层成像（fDOT）系统可以提供大量数据集，此外，待估计的未知参数数量众多，以至于灵敏度矩阵太大而无法存储。或者，可以使用迭代方法，但在处理大型矩阵时，它们的收敛速度可能非常慢。已经开发了一些适用于从非常大数据集重建图像的方法。然而，它们要么需要显式构建灵敏度矩阵，要么计算时间很慢，要么只能应用于受限的几何形状。我们引入了一种用于具有大数据集和求解空间的 fDOT 的快速重建方法，该方法在压缩表示的同时保留了正算子的分辨率。该方法不需要构建完整的矩阵，因此允许存储和直接反转显式构建的压缩系统矩阵。该方法使用模拟和实验数据进行了测试。结果表明，fDOT 图像重建问题可以有效地压缩，而不会丢失大量信息，并且具有降低图像噪声的优点。
