TY - GEN
T1 - Total variation regularization for bioluminescence tomography with an adaptive parameter choice approach
AU - Feng, Jinchao
AU - Jia, Xiaowei
AU - Jia, Kebin
AU - Qin, Chenghu
AU - Tian, Jie
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84863605749
U2 - 10.1109/IEMBS.2011.6090980
DO - 10.1109/IEMBS.2011.6090980
M3 - 会议稿件
C2 - 22255203
AN - SCOPUS:84863605749
SN - 9781424441211
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3946
EP - 3949
BT - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Y2 - 30 August 2011 through 3 September 2011
ER -