Northwest University, School of Information Sciences and Technology, Xi'an, China.
Shaanxi Normal University, School of Physics and Information Technology, Xi'an, China.
J Biomed Opt. 2017 Apr 1;22(4):45009. doi: 10.1117/1.JBO.22.4.045009.
Sparse regularization methods have been widely used in fluorescence molecular tomography (FMT) for stable three-dimensional reconstruction. Generally, ? 1 -regularization-based methods allow for utilizing the sparsity nature of the target distribution. However, in addition to sparsity, the spatial structure information should be exploited as well. A joint ? 1 and Laplacian manifold regularization model is proposed to improve the reconstruction performance, and two algorithms (with and without Barzilai–Borwein strategy) are presented to solve the regularization model. Numerical studies and in vivo experiment demonstrate that the proposed Gradient projection-resolved Laplacian manifold regularization method for the joint model performed better than the comparative algorithm for ? 1 minimization method in both spatial aggregation and location accuracy.
稀疏正则化方法已广泛应用于荧光分子断层成像（FMT）中，以实现稳定的三维重建。一般来说，基于?1-正则化的方法允许利用目标分布的稀疏性。然而，除了稀疏性之外，还应该利用空间结构信息。本文提出了一种联合?1 和拉普拉斯流形正则化模型，以提高重建性能，并提出了两种算法（带和不带 Barzilai–Borwein 策略）来求解正则化模型。数值研究和体内实验表明，所提出的用于联合模型的梯度投影解析拉普拉斯流形正则化方法在空间聚集和位置准确性方面均优于对比算法的?1 最小化方法。
