Three-dimensional Bioluminescence Tomography based on Bayesian approach

Abstract

Bioluminescence tomography (BLT) poses a typical ill-posed inverse problem with a large number of unknowns and a relatively limited number of boundary measurements. It is indispensable to incorporate a priori information into the inverse problem formulation in order to obtain viable solutions. In the paper, Bayesian approach has been firstly suggested to incorporate multiple types of a priori information for BLT reconstruction. Meanwhile, a generalized adaptive Gaussian Markov random field (GAGMRF) prior model for unknown source density estimation is developed to further reduce the ill-posedness of BLT on the basis of finite element analysis. Then the distribution of bioluminescent source can be acquired by maximizing the log posterior probability with respect to a noise parameter and the unknown source density. Furthermore, the use of finite element method makes the algorithm appropriate for complex heterogeneous phantom. The algorithm was validated by numerical simulation of a 3-D micro-CT mouse atlas and physical phantom experiment. The reconstructed results suggest that we are able to achieve high computational efficiency and accurate localization of bioluminescent source.

Multi-modality molecular imaging for gastric cancer research

“…In our previous studies, two kinds of regularization methods were utilized as the penalty function, including the Tikhonov regularization method and the spares regularization method [9][10][11][12][13][14][15][16][17][18][19][20] . For the Tikhonov regularization, the penalty function was defined as an l 2 norm of k S ; and for the sparse regularization, the penalty function was defined as an l 1 norm.…”

“…CLT is an emerging molecular imaging technology based on the well-known phenomenon of Cerenkov radiation 8 . Secondly, based on the home-developed tri-modality BLT/FMT/micro-CT system and the requirements of biomedical applications, we proposed several reconstruction algorithms for 3D optical imaging which have been well validated using the numerical simulations and small animal experiments [9][10][11][12][13][14][15][16][17][18][19][20] . In addition, based on the Monte Carlo method and free space light transport theory, we developed a simulation platform for optical imaging named molecular optical simulation environment (MOSE), which provides an accurate solution to light transport both in biological tissues and in free space 21,22 .…”

Spectrally resolved bioluminescence tomography with Huber-Markov random-field regularization

“…Since the bioluminescence experiment is generally operated at a low temperature, therefore, photon detection can be modeled using shot noise statistics, then the data likelihood log p(y|S) can be given by [12,13] …”

“…(12), the spectral projected gradientbased large-scale optimization algorithm is employed. As far as convergence criterion is concerned, the similar disposal methods are adopted according to the literature [13].…”

Novel registration for microcomputed tomography and bioluminescence imaging based on iterated optimal projection

“…where Ω and ∂Ω are the domain and its boundary, respectively; ΦðxÞ denotes the photon flux density In recent years, many algorithms have been developed to reconstruct the inner bioluminescent source, 6,[30][31][32] which were proven to be confident in bioluminescent tomography experiments not only on phantom models but also on mouse models. Here, we chose the most commonly used adaptive finite element method (FEM) to assess the impact of registration accuracy on reconstruction using the three transgenic mice.…”