Chinese Journal of Lasers, Volume. 45, Issue 3, 307005(2018)
Research Progress of Fluorescence Molecular Tomography in Image Resconstruction
Figures & Tables(5)
Fig. 1. Comparisons of fluorescence intensity on CCD detectors among pfMC model, dfMC model, and phantom experiments
Fig. 2. (a)(e) Simulated models; (b)(f) reconstructed results of IRL2 regularization; (c)(g) reconstructed results obtained by common L1 regularization; (d)(h) reconstructed results obtained by IRL1
Table 1. Comparisons of several deterministic models used in FMT
View tableTable 1. Comparisons of several deterministic models used in FMT
Item Spherical harmonics approximation Simplified spherical harmonics approximation Discrete ordinates method P1 P3 SP3 S N Method Radiance is expanded to the first order Radiance is expanded to the third order Replace the 1D derivatives with their 3D counterparts Full solid angle of 4π is divided to some number of discrete angular intervals Scope Tissue with high albedo, far-field light distribution No limitations No limitations Tissue with homogeneity or weak heterogeneity Computational accuracy Good Better Better Better Computational complexity Small Big Middle Big
Table 2. Comparisons of several fluorescence MC models used in FMT
View tableTable 2. Comparisons of several fluorescence MC models used in FMT
Item sfMC afMC mfMC pfMC dfMC Assumption None Born approximation Born approximation Born approximation None Computational accuracy Excellent Good Good Good Best Computational efficiency Poor Good Poor Best Best Space complexity Small Big Huge Middle Middle
Table 3. Direct regularization method and main optimization algorithms used in FMT
View tableTable 3. Direct regularization method and main optimization algorithms used in FMT
Item Tikhonov regularization Sparsity-promoting regularization Total variation regularization Term L 2 normL 1 normLp (0<p< 1) normSeminorm Scope No limitations Sparsely spatial distribution of fluorophores Sparsely spatial distribution of fluorophores No limitations Advantage Differentiable objective function Retaining the high-frequency information Stronger effect of sparsity than L 1Retaining the edge feature Disadvantage Filtering out the high frequency information and yielding smooth reconstructions Non-differentiable objective function and complex implement Non-differentiable objective function and complex implement Creating the staircase effect and non- differentiable objective function Main optimization algorithm Gauss-Newton, conjugate gradient, Levenberg-Marquardt Restarted nonlinear conjugate gradient, homotopy, and split Bregman Majorization- Minimization Newtontype iterations, augmented Lagrangian split, and split Bregman
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Deng Yong, Luo Qingming. Research Progress of Fluorescence Molecular Tomography in Image Resconstruction[J]. Chinese Journal of Lasers, 2018, 45(3): 307005
Paper Information
Special Issue:
Received: Nov. 1, 2017
Accepted: --
Published Online: Mar. 6, 2018
The Author Email: Qingming Luo (qluo@mail.hust.edu.cn)
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