Liver cancer is one of the most common malignant tumors worldwide. In order to enable the noninvasive detection of small liver tumors in mice, we present a parallel iterative shrinkage (PIS) algorithm for dual-modality tomography. It takes advantage of microcomputed tomography and multiview bioluminescence imaging, providing anatomical structure and bioluminescence intensity information to reconstruct the size and location of tumors. By incorporating prior knowledge of signal sparsity, we associate some mathematical strategies including specific smooth convex approximation, an iterative shrinkage operator, and affine subspace with the PIS method, which guarantees the accuracy, efficiency, and reliability for three-dimensional reconstruction. Then an in vivo experiment on the bead-implanted mouse has been performed to validate the feasibility of this method. The findings indicate that a tiny lesion less than 3 mm in diameter can be localized with a position bias no more than 1 mm; the computational efficiency is one to three orders of magnitude faster than the existing algorithms; this approach is robust to the different regularization parameters and the lp norms. Finally, we have applied this algorithm to another in vivo experiment on an HCCLM3 orthotopic xenograft mouse model, which suggests the PIS method holds the promise for practical applications of whole-body cancer detection.
Fluorescence molecular tomography (FMT) can three-dimensionally resolve molecular activities in in vivo small animal
through the reconstruction of the distribution of fluorescent probes. Due to large number of unknowns and limited
measurements from the surfaces of small animals, the FMT problem is often ill-posed and ill-conditioned. Though
various L2-norm regularizations can make the solution stable, they usually make the solution over-smoothed. During the
early stages of tumor detection, fluorescent sources that indicate the distribution of tumors are usually small and sparse,
which can be regarded as a type of a priori information. L1-norm regularizations have been incorporated to promote the
sparsity of optical tomographic problems. In this paper, an efficient method with the L1-norm regularization based on
coordinate descent is proposed to solve the FMT problem with extremely limited measurements. The proposed method
minimizes the objective by solving a sequence of scalar minimization subproblems in multi-variable minimization. Each
subproblem improves the estimate of the solution via minimizing along a determined coordinate with all other
coordinates fixed. This algorithm first updates the coordinate that makes the energy decrease the most. Non-existence of
matrix-vector multiplication in the iteration process makes the proposed algorithm time-efficient. To evaluate this
method, we compare it to the iterated-shrinkage-based algorithm with L1-norm regularization in numerical experiments.
The proposed algorithm is able to obtain satisfactory reconstruction results even when the measurements are very limited.
Besides, the proposed algorithm is about two orders of magnitude faster than the iterated-shrinkage-based algorithm,
which enables the proposed algorithm into practical applications.
Tomographic bioluminescence imaging (TBI), with visible light emission in living organisms, is an effective way of
molecular imaging, which allows for the study of ongoing tumor biological processes in vivo and non-invasively. This
newly developed technology enables three-dimensional accuracy localization and quantitative analysis of the target
tumor cells in small animal via reconstructing the images acquired by the high-resolution imaging system. Due to the
difficulty of reconstruction, which is often referred to an ill-posed inverse problem, continuous efforts are still made to
find more practical and efficient approaches. In this paper, an iteratively re-weighted minimization (IRM) has been
applied to reconstruct the entire source distribution, which is known as sparse signals, inside the target tissue with the
limited outgoing photon density on its boundary. By introducing a weight function into the objective function, we
convert the lp norm problem into a more simple form of l2 norm to reduce the computational complexity. The weight
function is updated in each iterative step to compute the final optimal solution more efficiently. This method is proved to
be robust to different parameters, and mouse experiments are conducted to validate the feasibility of IRM approach,
which is also reliable at whole-body imaging.
Generally, the performance of tomographic bioluminescence imaging is dependent on several factors, such as regularization parameters and initial guess of source distribution. In this paper, a global-inexact-Newton based reconstruction method, which is regularized by a dynamic sparse term, is presented for tomographic reconstruction. The proposed method can enhance higher imaging reliability and efficiency. In vivo mouse experimental reconstructions were performed to validate the proposed method. Reconstruction comparisons of the proposed method with other methods demonstrate the applicability on an entire region. Moreover, the reliable performance on a wide range of regularization parameters and initial unknown values were also investigated. Based on the in vivo experiment and a mouse atlas, the tolerance for optical property mismatch was evaluated with optical overestimation and underestimation. Additionally, the reconstruction efficiency was also investigated with different sizes of mouse grids. We showed that this method was reliable for tomographic bioluminescence imaging in practical mouse experimental applications.
KEYWORDS: In vivo imaging, Tomography, Bioluminescence, 3D modeling, Tissues, Animal model studies, Spherical lenses, Tissue optics, Biomedical optics, Liver
In vivo bioluminescence imaging (BLI) has played a more and more important role in biomedical research of
small animals. Tomographic bioluminescence imaging (TBI) further translates the BLI optical information into
three-dimensional bioluminescent source distribution, which could greatly facilitate applications in related studies.
Although the diffusion approximation (DA) is one of the most widely-used forward models, higher-order
approximations are still needed for in vivo small animal imaging. In this work, as a relatively accurate and
higher-order approximation theory, a simplified spherical harmonics approximation (SPN) is applied for heterogeneous
tomographic bioluminescence imaging in vivo. Furthermore, coupled with the SPN, a generalized graph
cuts optimization approach is utilized, making BLT reconstructions fast and suit for the whole body of small
animals. Heterogeneous in vivo experimental reconstructions via the higher-order approximation model demonstrate
higher tomographic imaging quality, which is shown the capability for practical biomedical tomographic
imaging applications.
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