基于自适应参数选择方法的生物发光断层成像的全变差正则化

Total variation regularization for bioluminescence tomography with an adaptive parameter choice approach.

作者信息

Feng Jinchao, Jia Xiaowei, Jia Kebin, Qin Chenghu, Tian Jie

机构信息

College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:3946-9. doi: 10.1109/IEMBS.2011.6090980.

DOI:10.1109/IEMBS.2011.6090980

PMID:22255203

Abstract

In this paper, we explore the application of total variation regularization method for bioluminescence tomography (BLT) with an adaptive regularization parameter choice approach. Since BLT is a seriously ill-posed problem, therefore, l(2) regularized methods are frequently adopted to recover the bi-oluminescent sources. However, l(2) regularized methods typically lead to smooth reconstructions. In this paper, we investigated the use of total variation (TV) regularization to improve the quality of BLT reconstruction. Furthermore, the regularization parameter in TV method was chosen adaptively to make the proposed algorithm more stable. Results on simulation data provide evidence that the reconstructed source can be localized accurately compared with l(2) method. Meanwhile, the effectiveness of utility of the parameter choice were illustrated. Finally, different levels of noisy data were added to validate the performance of the proposed algorithm.

摘要

在本文中，我们探索了全变差正则化方法在生物发光断层成像（BLT）中的应用，并采用了一种自适应正则化参数选择方法。由于BLT是一个严重不适定问题，因此，常采用l(2)正则化方法来恢复生物发光源。然而，l(2)正则化方法通常会导致重建结果平滑。在本文中，我们研究了使用全变差（TV）正则化来提高BLT重建的质量。此外，TV方法中的正则化参数是自适应选择的，以使所提出的算法更稳定。模拟数据结果表明，与l(2)方法相比，重建源能够被精确地定位。同时，也说明了参数选择方法的有效性。最后，添加不同水平的噪声数据来验证所提出算法的性能。