Shang Shang, Bai Jing, Song Xiaolei, Wang Hongkai, Lau Jaclyn
Medical Engineering and Health Technology Research Group, Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
Int J Biomed Imaging. 2007;2007:84724. doi: 10.1155/2007/84724.
Conjugate gradient method is verified to be efficient for nonlinear optimization problems of large-dimension data. In this paper, a penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography (FMT) is presented. The algorithm combines the linear conjugate gradient method and the nonlinear conjugate gradient method together based on a restart strategy, in order to take advantage of the two kinds of conjugate gradient methods and compensate for the disadvantages. A quadratic penalty method is adopted to gain a nonnegative constraint and reduce the illposedness of the problem. Simulation studies show that the presented algorithm is accurate, stable, and fast. It has a better performance than the conventional conjugate gradient-based reconstruction algorithms. It offers an effective approach to reconstruct fluorochrome information for FMT.
共轭梯度法被证明对大尺寸数据的非线性优化问题是有效的。本文提出了一种用于荧光分子断层成像（FMT）重建的惩罚线性与非线性组合共轭梯度法。该算法基于重启策略将线性共轭梯度法和非线性共轭梯度法结合在一起，以便利用这两种共轭梯度法的优点并弥补其缺点。采用二次惩罚法来获得非负约束并降低问题的不适定性。仿真研究表明，所提出的算法准确、稳定且快速。它比传统的基于共轭梯度的重建算法具有更好的性能。它为FMT重建荧光染料信息提供了一种有效的方法。
