Image reconstruction for bioluminescence tomography from partial measurement

Ji, Ming; Zhou, Tong; Cheng, Jianqiang; Cong, Wenxiang; Wang, Ge

doi:10.1364/oe.15.011095

Cited by 47 publications

(62 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The configuration of the phantom and form of sources used for numerical experiments are the same as in our previous study [9]. Due to the limited space, we only report representative reconstruction results of S-BLT with the EM algorithm as the underlying inner-loop iterative algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…However, BLI only works in 2D imaging mode which is mainly qualitative and incapable of 3D imaging in practice. To overcome limitations of BLI, bioluminescence tomography (BLT) was introduced [6] to perform quantitative 3D reconstruction of bioluminescent source distributions in vivo and has undergone intensive research since [7][8] [9][10] [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Heuristic Superiorization-Like Approach to Bioluminescence Tomography

2013

Self Cite

View full text Add to dashboard Cite

Abstract-Bioluminescence tomography (BLT) is a powerful molecular imaging technology designed for the localization and quantification of bioluminescent sources in vivo. With the forward process modeled by the diffusion approximation equation, BLT is the inverse problem to reconstruct the distribution of internal bioluminescent sources subject to Cauchy data. Due to the non-uniqueness of BLT in general, adequate prior information such as nonnegativity and source support constraints must be utilized to obtain a physically favorable BLT solution. Iterative algorithms such as the well-known Expectation Maximization (EM) algorithm and the Landweber algorithm which are suitable for incorporating these knowledge-based constraints are widely used in practice. In the current work, we investigate the application of a superiorization-like approach to BLT. A superiorization-like version of prototypical iterative algorithms for BLT in a general framework, denoted by S-BLT, is presented. For the EM algorithm as the underlying iterative algorithms for BLT and S-BLT, superiorized by the total variation (TV) merit function, preliminary simulation results for a heterogeneous phantom are reported to demonstrate the viability of the approach and evaluate the performance of the proposed algorithm. It is found that total variation superiorization of BLT can significantly improve the visualization effect of the reconstruction with the sources set as a particular case of radial basis functions.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Heuristic Superiorization-Like Approach to Bioluminescence Tomography

2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many important results in this area are bought out by several groups in the world [5,7,12,16,17]. However, BLT is still faced with challenge, such as more accurate mathematical model, the improvement of the reconstruction algorithm, the deeper depth reconstruction of small animal, and so on.…”

Section: Discussionmentioning

confidence: 99%

Spatial Weighed Element Based FEM Incorporating a Priori Information on Bioluminescence Tomography

Shi

Tian

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

View full text Add to dashboard Cite

Abstract. Bioluminescence tomography (BLT) is a promising imaging technique which may dynamically and real-timely detect the molecular and cellular activity at the whole-body level in small animal studies. In view of the ill-posedness of the BLT, it is hard to fully reconstruct source density. In addition, the uniqueness theorem on BLT indicates that it is important to employ a priori information for accurate source reconstruction. Hence, we adopt diffuse optical tomography (DOT) technique to provide optical parameters of main tissues as a priori information. Besides, we restrict the range of real source to a permissible region to raise the numerical stability and reduce the ill-posedness of BLT. Next, we forward Spatial Weighed Element based Finite Element Method and compare it's solutions with analytic formula and MOSE. Numerical simulation of homogeneous and heterogeneous phantom demonstrates the source location and density with prior information is better than that not using a priori information.Keywords: bioluminescence tomography (BLT), diffuse optical tomography (DOT), spatial weighed element based FEM, a priori information.

show abstract

“…Although the problem size can be reduced by allowing sources only in a permissible region (Cong et al 2004, 2005, Wang et al 2006a, 2006b, Jiang et al 2007, Lv et al 2007, we do not consider such constraints here. In (11), L is the data-fit function (12) β is a regularization parameter, and R is a quadratic regularization function such that (13) for a nonnegative definite matrix .…”

Section: Regularized Least Squares (Rls)mentioning

confidence: 99%

Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography

et al. 2008

View full text Add to dashboard Cite

We investigate fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography for applications in small animal imaging. Our forward model uses a diffusion approximation for optically inhomogeneous tissue, which we solve using a finite element method (FEM). We examine two approaches to incorporating the forward model into the solution of the inverse problem. In a conventional direct calculation approach one computes the full forward model by repeated solution of the FEM problem, once for each potential source location. We describe an alternative on-the-fly approach where one does not explicitly solve for the full forward model. Instead, the solution to the forward problem is included implicitly in the formulation of the inverse problem, and the FEM problem is solved at each iteration for the current image estimate. We evaluate the convergence speeds of several representative iterative algorithms. We compare the computation cost of those two approaches, concluding that the onthe-fly approach can lead to substantial reductions in total cost when combined with a rapidly converging iterative algorithm.

show abstract

Image reconstruction for bioluminescence tomography from partial measurement

Cited by 47 publications

References 49 publications

A Heuristic Superiorization-Like Approach to Bioluminescence Tomography

A Heuristic Superiorization-Like Approach to Bioluminescence Tomography

Spatial Weighed Element Based FEM Incorporating a Priori Information on Bioluminescence Tomography

Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography

Contact Info

Product

Resources

About