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Truncated total least squares method with a practical truncation parameter choice scheme for bioluminescence tomography inverse problem.

He X, Liang J, Qu X, Huang H, Hou Y, Tian J - Int J Biomed Imaging (2010)

Bottom Line: Most regularization techniques such as Tikhonov method only consider measurement noise, whereas the influences of system errors have not been investigated.Based on the modified generalized cross validation (MGCV) criterion and residual error minimization, a practical parameter-choice scheme referred to as improved GCV (IGCV) is proposed for TTLS.Numerical simulations with different noise levels and physical experiments demonstrate the effectiveness and potential of TTLS combined with IGCV for solving the BLT inverse problem.

View Article: PubMed Central - PubMed

Affiliation: Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, China.

ABSTRACT
In bioluminescence tomography (BLT), reconstruction of internal bioluminescent source distribution from the surface optical signals is an ill-posed inverse problem. In real BLT experiment, apart from the measurement noise, the system errors caused by geometry mismatch, numerical discretization, and optical modeling approximations are also inevitable, which may lead to large errors in the reconstruction results. Most regularization techniques such as Tikhonov method only consider measurement noise, whereas the influences of system errors have not been investigated. In this paper, the truncated total least squares method (TTLS) is introduced into BLT reconstruction, in which both system errors and measurement noise are taken into account. Based on the modified generalized cross validation (MGCV) criterion and residual error minimization, a practical parameter-choice scheme referred to as improved GCV (IGCV) is proposed for TTLS. Numerical simulations with different noise levels and physical experiments demonstrate the effectiveness and potential of TTLS combined with IGCV for solving the BLT inverse problem.

No MeSH data available.


(a) A cross-section through two luminescent sources (S) in the left lung of a mouse phantom consisting of bone (B), heart (H), lungs (L), and tissue (T). (b) A 3D view of the permissible region.
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fig1: (a) A cross-section through two luminescent sources (S) in the left lung of a mouse phantom consisting of bone (B), heart (H), lungs (L), and tissue (T). (b) A 3D view of the permissible region.

Mentions: In the numerical simulation, a 30 mm diameter and 30 mm high cylindrical mouse chest phantom is designed to evaluate the performance of the reconstruction method. The structure of the phantom is shown in Figure 1(a). The phantom is heterogeneous and the corresponding optical parameters are set as in Table 1 [25]. Two sphere sources of 0.5 mm diameter with 1 nW/mm3energy density are located in the left lung and the centers are S1 = (−9 mm, −1.5 mm, 15 mm) and S2 = (−9 mm, 1.5 mm, 15 mm), respectively. The power of each source is 0.5236 nW. In the following single source case, only the source centered at S1 is considered.


Truncated total least squares method with a practical truncation parameter choice scheme for bioluminescence tomography inverse problem.

He X, Liang J, Qu X, Huang H, Hou Y, Tian J - Int J Biomed Imaging (2010)

(a) A cross-section through two luminescent sources (S) in the left lung of a mouse phantom consisting of bone (B), heart (H), lungs (L), and tissue (T). (b) A 3D view of the permissible region.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2874932&req=5

fig1: (a) A cross-section through two luminescent sources (S) in the left lung of a mouse phantom consisting of bone (B), heart (H), lungs (L), and tissue (T). (b) A 3D view of the permissible region.
Mentions: In the numerical simulation, a 30 mm diameter and 30 mm high cylindrical mouse chest phantom is designed to evaluate the performance of the reconstruction method. The structure of the phantom is shown in Figure 1(a). The phantom is heterogeneous and the corresponding optical parameters are set as in Table 1 [25]. Two sphere sources of 0.5 mm diameter with 1 nW/mm3energy density are located in the left lung and the centers are S1 = (−9 mm, −1.5 mm, 15 mm) and S2 = (−9 mm, 1.5 mm, 15 mm), respectively. The power of each source is 0.5236 nW. In the following single source case, only the source centered at S1 is considered.

Bottom Line: Most regularization techniques such as Tikhonov method only consider measurement noise, whereas the influences of system errors have not been investigated.Based on the modified generalized cross validation (MGCV) criterion and residual error minimization, a practical parameter-choice scheme referred to as improved GCV (IGCV) is proposed for TTLS.Numerical simulations with different noise levels and physical experiments demonstrate the effectiveness and potential of TTLS combined with IGCV for solving the BLT inverse problem.

View Article: PubMed Central - PubMed

Affiliation: Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, China.

ABSTRACT
In bioluminescence tomography (BLT), reconstruction of internal bioluminescent source distribution from the surface optical signals is an ill-posed inverse problem. In real BLT experiment, apart from the measurement noise, the system errors caused by geometry mismatch, numerical discretization, and optical modeling approximations are also inevitable, which may lead to large errors in the reconstruction results. Most regularization techniques such as Tikhonov method only consider measurement noise, whereas the influences of system errors have not been investigated. In this paper, the truncated total least squares method (TTLS) is introduced into BLT reconstruction, in which both system errors and measurement noise are taken into account. Based on the modified generalized cross validation (MGCV) criterion and residual error minimization, a practical parameter-choice scheme referred to as improved GCV (IGCV) is proposed for TTLS. Numerical simulations with different noise levels and physical experiments demonstrate the effectiveness and potential of TTLS combined with IGCV for solving the BLT inverse problem.

No MeSH data available.