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Filtered maximum likelihood expectation maximization based global reconstruction for bioluminescence tomography

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Abstract

The reconstruction of bioluminescence tomography (BLT) is severely ill-posed due to the insufficient measurements and diffuses nature of the light propagation. Predefined permissible source region (PSR) combined with regularization terms is one common strategy to reduce such ill-posedness. However, the region of PSR is usually hard to determine and can be easily affected by subjective consciousness. Hence, we theoretically developed a filtered maximum likelihood expectation maximization (fMLEM) method for BLT. Our method can avoid predefining the PSR and provide a robust and accurate result for global reconstruction. In the method, the simplified spherical harmonics approximation (SPN) was applied to characterize diffuse light propagation in medium, and the statistical estimation-based MLEM algorithm combined with a filter function was used to solve the inverse problem. We systematically demonstrated the performance of our method by the regular geometry- and digital mouse-based simulations and a liver cancer-based in vivo experiment.

The filtered MLEM-based global reconstruction method for BLT.

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Funding

This work was supported by Zhejiang Province Nature Science Foundation of China LR17F030006 and the National Natural Science Foundation of China under Grant Nos. 61671196, 81571725, 61601154, 81627807, 61327902, and 61372046.

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Correspondence to Xueli Chen.

Ethics declarations

In in vivo experiments, all of the animal procedures were performed according to the guidance of the Institutional Animal Care and Use Committee at Peking University (Permit Number. 2011-0039).

Appendix

Appendix

The detailed expressions of Mand F are as follows:

$$ \left\{\begin{array}{l}{M}_{m,n}^{1,1}={\int}_{\Omega}\left(\frac{1}{3{\mu}_{a1}}\nabla {\psi}_m(r)\nabla {\psi}_n(r)+{\mu}_a{\psi}_m(r){\psi}_n(r)\right) dr-{\int}_{\partial \varOmega}\left(\frac{\xi_{11}}{3{\mu}_{a1}}{\psi}_m(r){\psi}_n(r)\right) dr\\ {}{M}_{m,n}^{1,2}=-{\int}_{\partial \varOmega}\left(\frac{\xi_{12}}{3{\mu}_{a1}}{\psi}_m(r){\psi}_n(r)\right) dr\\ {}{M}_{m,n}^{2,1}=-{\int}_{\varOmega}\left(\frac{2{\mu}_a}{3}{\psi}_m(r){\psi}_n(r)\right) dr-{\int}_{\partial \varOmega}\left(\frac{\xi_{21}}{7{\mu}_{a3}}{\psi}_m(r){\psi}_n(r)\right) dr\\ {}{M}_{m,n}^{2,2}=-{\int}_{\Omega}\left(\frac{1}{7{\mu}_{a3}}\nabla {\psi}_m(r)\nabla {\psi}_n(r)+\left(\frac{4}{9}{\mu}_a+\frac{5}{9}{\mu}_{a2}\right){\psi}_m(r){\psi}_n(r)\right) dr-{\int}_{\partial \varOmega}\left(\frac{\xi_{22}}{7{\mu}_{a3}}{\psi}_m(r){\psi}_n(r)\right) dr\\ {}{F}_{m,n}^{11}={\int}_{\Omega}{\psi}_m(r){\psi}_n(r) dr\\ {}{F}_{m,n}^{22}=\hbox{-} \frac{2}{3}{\int}_{\Omega}{\psi}_m(r){\psi}_n(r) dr\end{array}\right. $$
(14)

where ξ11, ξ12, ξ21, ξ22 could be deduced according to Eq. (1) and could be found in reference [8]. And the coefficients βi (i = 1, 2) in Eq. (3) could be deduced as follows:

$$ \left\{\begin{array}{l}{\beta}_1=\frac{1}{4}+{J}_0-\left(\frac{0.5+{J}_1}{3{\mu}_{a1}}\right){\xi}_{11}-\left(\frac{J_3}{7{\mu}_{a3}}\right){\xi}_{21}\\ {}{\beta}_2=-\frac{1}{16}-\frac{2}{3}{J}_0+\frac{1}{3}{J}_2-\left(\frac{0.5+{J}_1}{3{\mu}_{a1}}\right){\xi}_{12}-\left(\frac{J_3}{7{\mu}_{a3}}\right){\xi}_{22}\end{array}\right. $$
(15)

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Yang, D., Wang, L., Chen, D. et al. Filtered maximum likelihood expectation maximization based global reconstruction for bioluminescence tomography. Med Biol Eng Comput 56, 2067–2081 (2018). https://doi.org/10.1007/s11517-018-1842-z

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