IEEE Trans Biomed Eng. 2019 May;66(5):1468-1476. doi: 10.1109/TBME.2018.2874699. Epub 2018 Oct 8.
Fluorescence Molecular Tomography (FMT) is a promising optical tool for small animal imaging. The l-norm regularization has attracted attention in the field of FMT due to its ability in enhancing sparsity of solution and coping with the high ill-posedness of the inverse problem. However, efficient algorithm for solving the nonconvex regularized model deserve to explore.
A Half Thresholding Pursuit Algorithm (HTPA) combined with parameter optimization is proposed in this paper to efficiently solve the nonconvex optimization model. Specifically, the half thresholding iteration method is utilized to solve l-norm model, pursuit strategy is used to accelerate the process of iteration, and the parameter optimization scheme is designed to obtain robust parameter.
Analysis and assessment on simulated and experimental data demonstrate that the proposed HTPA performs better in location accuracy and reconstructed fluorescent yield in less time cost, compared with the state-of-the-art reconstruction algorithms.
The proposed HTPA combined with the parameter optimization scheme is an efficient and robust reconstruction approach to FMT.
荧光分子断层扫描（FMT）是一种很有前途的小动物成像光学工具。由于 l-范数正则化能够增强解的稀疏性并应对反问题的高度不适定性，因此在 FMT 领域引起了关注。然而，值得探索用于解决非凸正则化模型的有效算法。
本文提出了一种结合参数优化的半阈值追踪算法（HTPA），以有效地解决非凸优化模型。具体来说，利用半阈值迭代方法求解 l-范数模型，使用追踪策略加速迭代过程，并设计参数优化方案以获得稳健的参数。
对模拟数据和实验数据的分析和评估表明，与最先进的重建算法相比，所提出的 HTPA 在更低的时间成本下，在定位准确性和重建荧光产率方面表现更好。
所提出的结合参数优化方案的 HTPA 是一种高效、稳健的 FMT 重建方法。
