Chen Maomao, Su Han, Zhou Yuan, Cai Chuangjian, Zhang Dong, Luo Jianwen
Tsinghua University, School of Medicine, Department of Biomedical Engineering, Beijing 100084, China.
Tsinghua University, School of Medicine, Department of Biomedical Engineering, Beijing 100084, China; Tsinghua University, Center for Biomedical Imaging Research, Beijing, 100084, China.
Biomed Opt Express. 2016 Nov 9;7(12):5021-5041. doi: 10.1364/BOE.7.005021. eCollection 2016 Dec 1.
Dynamic fluorescence molecular tomography (FMT) is a promising technique for the study of the metabolic process of fluorescent agents in the biological body , 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 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.
动态荧光分子断层扫描（FMT）是一种用于研究荧光剂在生物体内代谢过程的很有前景的技术，参数图像的质量在很大程度上依赖于重建的FMT图像的准确性。在典型的动态FMT实现中，对成像对象进行连续50多分钟的监测。在每分钟内，采集一组荧光测量数据并重建相应的FMT图像。在每个FMT图像的重建中手动设置正则化参数是困难的。本文通过数值模拟、体模实验和实验对用L曲线和U曲线方法获得的参数图像进行了定量评估。结果表明，U曲线方法在参数成像中获得了更好的准确性、更强的鲁棒性和更高的抗噪声能力。因此，它是一种用于动态FMT正则化参数自动选择的很有前景的方法。
