Comparison of Regularization Methods in Fluorescence Molecular Tomography

@article{Zhu2014ComparisonOR,
  title={Comparison of Regularization Methods in Fluorescence Molecular Tomography},
  author={Dianwen Zhu and Yue Zhao and Reheman Baikejiang and Zhen Yuan and Changqing Li},
  journal={Photonics},
  year={2014},
  volume={1},
  pages={95-109}
}
In vivo fluorescence molecular tomography (FMT) has been a popular functional imaging modality in research labs in the past two decades. One of the major difficulties of FMT lies in the ill-posed and ill-conditioned nature of the inverse problem in reconstructing the distribution of fluorophores inside objects. The popular regularization methods based on L2, L1 and total variation (TV ) norms have been applied in FMT reconstructions. The non-convex Lq(0 < q < 1) semi-norm and Log function have… 

Improved sparse reconstruction for fluorescence molecular tomography with L1/2 regularization.

The proposed L1/2-norm method outperformed the comparative L1-norm reconstruction methods in terms of location accuracy, spatial resolution and quantitation of fluorescent yield and simulation analysis showed the robustness of the proposed method, under different levels of measurement noise and number of excitation sources.

Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method

Numerical simulation results indicate that the proposed kernel method can separate two FMT targets with an edge-to-edge distance of 1 mm and is robust to false-positive guidance and inhomogeneity in the anatomical image.

Robust reconstruction of fluorescence molecular tomography with an optimized illumination pattern

Fluorescence molecular tomography (FMT) is an emerging powerful tool for biomedical research and drug development. The reconstruction quality of the unknown fluorescence distribution is the focus of

Nonuniform update for sparse target recovery in fluorescence molecular tomography accelerated by ordered subsets.

It is found that the proposed nonuniform updating method outperforms its popular uniform counterpart by obtaining a more localized, less noisy, more accurate image and the computational cost was greatly reduced.

Accelerated image reconstruction in fluorescence molecular tomography using a nonuniform updating scheme with momentum and ordered subsets methods

This work systematically compared the effects of the momentum technique, the OS method, and the nonuniform updating scheme in accelerating the FMT reconstruction and found that the proposed combined method can produce a high-quality image using an order of magnitude less time.

Robust reconstruction of fluorescence molecular tomography with an optimized illumination pattern

This work takes advantage of the discrete formulation of the forward problem to give a rigorous definition of an illumination pattern as well as the admissible set of patterns, and adds restrictions in theAdmissible set as different types of regularizers to a discrepancy functional, generating another inverse problem with the illumination patterns as unknown.

Application of kernel method in fluorescence molecular tomography

The proposed kernel-based algorithm can improve the spatial resolution of the reconstructed FMT images and can be obtained directly from the anatomical image and is included in the forward modeling.

Accelerating spatially non-uniform update for sparse target recovery in fluorescence molecular tomography by ordered subsets and momentum methods

This paper proposes to further enhance the convergence speed by incorporating the first order momentum method that uses previous iterations to achieve a quadratic convergence rate, and shows that the proposed method indeed leads to a much faster convergence.

Direct reconstruction of pharmacokinetic parameters in dynamic fluorescence molecular tomography by the augmented Lagrangian method

This paper proposes to take advantage of both the direct and indirect reconstruction ideas through a variable splitting strategy under the augmented Lagrangian framework and finds that the proposed algorithm can achieve good reconstruction results within a small amount of time.

Fluorescence Molecular Tomography Based on Group Sparsity Priori for Morphological Reconstruction of Glioma

Group sparsity priori can effectively improve the morphological accuracy of FMT reconstruction, which is of great practical significance in tumor research.

References

SHOWING 1-10 OF 33 REFERENCES

Reconstruction algorithms based on l1-norm and l2-norm for two imaging models of fluorescence molecular tomography: a comparative study

A comparative study between the reconstruction algorithms based on l1-norm and l2-norm for two imaging models of FMT is presented, indicating that l 1-norm regularization is more suitable for reconstructing the small fluorescent target, while l 2- norm regularization performs better for the reconstruction of the distribution of fluorescent substance.

Joint L1 and total variation regularization for fluorescence molecular tomography

A surrogate-based optimization method for minimizing the joint penalties involving a combination of the L(1) and total variation norm penalties, the former to suppress spurious background signals and enforce sparsity and the latter to preserve local smoothness and piecewise constancy in the reconstructed images.

Total variation regularization for 3D reconstruction in fluorescence tomography: experimental phantom studies.

This work has developed two iterative methods for fast 3D reconstruction in FT based on TV regularization inspired by Rudin-Osher-Fatemi and split Bregman algorithms and observed that the proposed method performs better in resolving fluorescence inclusions at different depths.

Data Specific Spatially Varying Regularization for Multimodal Fluorescence Molecular Tomography

A new, two step approach to incorporating structural priors into the FMT inverse problem is presented, using the anatomic information to define a low dimensional inverse problem, and obtaining a solution which is then used to determine the parameters defining a spatially varying regularization matrix for the full resolution problem.

Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement

A majorization-minimization algorithm for the iterative reconstruction process is adopted and it is found that the proposed nonconvex methods outperform L(1) regularization in accurately recovering sparse targets in FMT.

A fast reconstruction algorithm for fluorescence molecular tomography with sparsity regularization.

An FMT reconstruction algorithm based on the iterated shrinkage method that can obtain more accurate results, even with very limited measurement data is proposed.

Efficient L1 regularization-based reconstruction for fluorescent molecular tomography using restarted nonlinear conjugate gradient.

An efficient L1 regularization-based reconstruction algorithm based on nonlinear conjugate gradient with restarted strategy is proposed to increase the computational speed with low memory consumption and obtain high spatial resolution and high signal-to-noise ratio.

Spatially varying regularization based on spectrally resolved fluorescence emission in fluorescence molecular tomography.

The results retrieved through tissue phantom experiments imply that initial reconstructions with spatially varying priors reduces artifacts and show slightly more accurate reconstruction results compared to reconstructions using no priors.

Improvement of image quality of time-domain diffuse optical tomography with lp sparsity regularization

Numerical experiments show that the l p sparsity regularization improves the spatial resolution and recovers the difference in the absorption coefficients between two targets, although a target with a small absorption coefficient may disappear due to the strong effect of the lp sparsityRegularization when the value of p is too small.

Simultaneous PET and Multispectral 3-Dimensional Fluorescence Optical Tomography Imaging System

Phantom and in vivo experiments demonstrate the feasibility of simultaneous PET and 3D FOT imaging.