Automatic selection of regularization parameters for dynamic fluorescence molecular tomography: a comparison of L-curve and U-curve methods
Automatic selection of regularization parameters for dynamic fluorescence molecular tomography: a comparison of L-curve and U-curve methods
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DOI：
10.1364/boe.7.005021
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发表时间：
2016-12-01
影响因子：
3.4
通讯作者：
Luo, Jianwen
中科院分区：
文献类型：
--
作者：
Chen, Maomao;Su, Han;Luo, Jianwen
动态荧光分子断层扫描（FMT）是研究体内生物体内荧光剂代谢过程的一项有前途的技术，参数图像的质量在很大程度上依赖于重建FMT图像的准确性。在典型的动态 FMT 实施中，成像对象会被连续监测 50 分钟以上。在每一分钟内，都会采集一组荧光测量结果并重建相应的 FMT 图像。在每幅FMT图像的重建中，手动设置正则化参数是很困难的。本文通过数值模拟、体模实验和体内实验对L曲线和U曲线方法获得的参数图像进行定量评估。结果表明，U曲线方法在参量成像中具有更好的精度、更强的鲁棒性和更高的抗噪声能力。因此，它是一种自动选择动态 FMT 正则化参数的有前途的方法。 (C) 2016年美国光学学会
Dynamic fluorescence molecular tomography (FMT) is a promising technique for the study of the metabolic process of fluorescent agents in the biological body in vivo, and the quality of the parametric images relies heavily on the accuracy of the reconstructed FMT images. In typical dynamic FMT implementations, the imaged object is continuously monitored for more than 50 minutes. During each minute, a set of the fluorescent measurements is acquired and the corresponding FMT image is reconstructed. It is difficult to manually set the regularization parameter in the reconstruction of each FMT image. In this paper, the parametric images obtained with the L-curve and U-curve methods are quantitatively evaluated through numerical simulations, phantom experiments and in vivo experiments. The results illustrate that the U-curve method obtains better accuracy, stronger robustness and higher noise-resistance in parametric imaging. Therefore, it is a promising approach to automatic selection of the regularization parameters for dynamic FMT. (C) 2016 Optical Society of America