T. Liu, J. Rong, P. Gao, W. Zhang, W. Liu, Y. Zhang, and H. Lu, “Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage,” J. Biomed. Opt. 23(2), 1–11 (2018).

[Crossref]
[PubMed]

P. Gao, H. Pu, J. Rong, W. Zhang, T. Liu, W. Liu, Y. Zhang, and H. Lu, “Resolving adjacent nanophosphors of different concentrations by excitation-based cone-beam X-ray luminescence tomography,” Biomed. Opt. Express 8(9), 3952–3965 (2017).

[Crossref]
[PubMed]

R. Baikejiang, Y. Zhao, B. Z. Fite, K. W. Ferrara, and C. Li, “Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method,” J. Biomed. Opt. 22(5), 55001 (2017).

[Crossref]
[PubMed]

G. Zhang, F. Liu, J. Liu, J. Luo, Y. Xie, J. Bai, and L. Xing, “Cone beam x-ray luminescence computed tomography based on Bayesian method,” IEEE Trans. Med. Imaging 36(1), 225–235 (2017).

[Crossref]
[PubMed]

X. Liu, Q. Liao, and H. Wang, “Fast X-ray luminescence computed tomography imaging,” IEEE Trans. Biomed. Eng. 61(6), 1621–1627 (2014).

[Crossref]
[PubMed]

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).

[Crossref]
[PubMed]

D. Chen, S. Zhu, H. Yi, X. Zhang, D. Chen, J. Liang, and J. Tian, “Cone beam x-ray luminescence computed tomography: a feasibility study,” Med. Phys. 40(3), 031111 (2013).

[Crossref]
[PubMed]

X. Liu, Q. Liao, and H. Wang, “In vivo x-ray luminescence tomographic imaging with single-view data,” Opt. Lett. 38(22), 4530–4533 (2013).

[Crossref]
[PubMed]

D. Dong, S. Zhu, C. Qin, V. Kumar, J. V. Stein, S. Oehler, C. Savakis, J. Tian, and J. Ripoll, “Automated recovery of the center of rotation in optical projection tomography in the presence of scattering,” IEEE J. Biomed. Health Inform. 17(1), 198–204 (2013).

[Crossref]
[PubMed]

J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57(6), 1459–1476 (2012).

[Crossref]
[PubMed]

Y. Liu, J. Ma, Y. Fan, and Z. Liang, “Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction,” Phys. Med. Biol. 57(23), 7923–7956 (2012).

[Crossref]
[PubMed]

J. Feng, C. Qin, K. Jia, S. Zhu, X. Yang, and J. Tian, “Bioluminescence tomography imaging in vivo: recent advances,” IEEE J Sel Top Quant 18(4), 1394–1402 (2012).

[Crossref]

Z. Tian, X. Jia, K. Yuan, T. Pan, and S. B. Jiang, “Low-dose CT reconstruction via edge-preserving total variation regularization,” Phys. Med. Biol. 56(18), 5949–5967 (2011).

[Crossref]
[PubMed]

M. Defrise, C. Vanhove, and X. Liu, “An algorithm for total variation regularization in high-dimensional linear problems,” Inverse Probl. 27(6), 065002 (2011).

[Crossref]

C. M. Carpenter, G. Pratx, C. Sun, and L. Xing, “Limited-angle x-ray luminescence tomography: methodology and feasibility study,” Phys. Med. Biol. 56(12), 3487–3502 (2011).

[Crossref]
[PubMed]

H. Gao and H. Zhao, “Multilevel bioluminescence tomography based on radiative transfer equation Part 1: l1 regularization,” Opt. Express 18(3), 1854–1871 (2010).

[Crossref]
[PubMed]

G. Pratx, C. M. Carpenter, C. Sun, and L. Xing, “X-ray luminescence computed tomography via selective excitation: a feasibility study,” IEEE Trans. Med. Imaging 29(12), 1992–1999 (2010).

[Crossref]
[PubMed]

C. M. Carpenter, C. Sun, G. Pratx, R. Rao, and L. Xing, “Hybrid x-ray/optical luminescence imaging: characterization of experimental conditions,” Med. Phys. 37(8), 4011–4018 (2010).

[Crossref]
[PubMed]

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci. 2(1), 183–202 (2009).

[Crossref]

S. Zhu, J. Tian, G. Yan, C. Qin, and J. Feng, “Cone beam micro-CT system for small animal imaging and performance evaluation,” Int. J. Biomed. Imaging 2009, 960573 (2009).

[Crossref]
[PubMed]

E. J. Candès and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).

[Crossref]

E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. 53(17), 4777–4807 (2008).

[Crossref]
[PubMed]

M. A. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007).

[Crossref]

E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. XRay Sci. Technol. 14, 119–139 (2006).

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006).

[Crossref]

Y. Lv, J. Tian, W. Cong, G. Wang, J. Luo, W. Yang, and H. Li, “A multilevel adaptive finite element algorithm for bioluminescence tomography,” Opt. Express 14(18), 8211–8223 (2006).

[Crossref]
[PubMed]

I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math. 57(11), 1413–1457 (2004).

[Crossref]

B. K. Natarajan, “Sparse approximate solutions to linear systems,” SIAM J. Comput. 24(2), 227–234 (1995).

[Crossref]

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The finite element method for the propagation of light in scattering media: boundary and source conditions,” Med. Phys. 22(11 Pt 1), 1779–1792 (1995).

[Crossref]
[PubMed]

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60(1-4), 259–268 (1992).

[Crossref]

T. Hebert and R. Leahy, “A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors,” IEEE Trans. Med. Imaging 8(2), 194–202 (1989).

[Crossref]
[PubMed]

J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57(6), 1459–1476 (2012).

[Crossref]
[PubMed]

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The finite element method for the propagation of light in scattering media: boundary and source conditions,” Med. Phys. 22(11 Pt 1), 1779–1792 (1995).

[Crossref]
[PubMed]

G. Zhang, F. Liu, J. Liu, J. Luo, Y. Xie, J. Bai, and L. Xing, “Cone beam x-ray luminescence computed tomography based on Bayesian method,” IEEE Trans. Med. Imaging 36(1), 225–235 (2017).

[Crossref]
[PubMed]

R. Baikejiang, Y. Zhao, B. Z. Fite, K. W. Ferrara, and C. Li, “Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method,” J. Biomed. Opt. 22(5), 55001 (2017).

[Crossref]
[PubMed]

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci. 2(1), 183–202 (2009).

[Crossref]

E. J. Candès and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).

[Crossref]

C. M. Carpenter, G. Pratx, C. Sun, and L. Xing, “Limited-angle x-ray luminescence tomography: methodology and feasibility study,” Phys. Med. Biol. 56(12), 3487–3502 (2011).

[Crossref]
[PubMed]

G. Pratx, C. M. Carpenter, C. Sun, and L. Xing, “X-ray luminescence computed tomography via selective excitation: a feasibility study,” IEEE Trans. Med. Imaging 29(12), 1992–1999 (2010).

[Crossref]
[PubMed]

C. M. Carpenter, C. Sun, G. Pratx, R. Rao, and L. Xing, “Hybrid x-ray/optical luminescence imaging: characterization of experimental conditions,” Med. Phys. 37(8), 4011–4018 (2010).

[Crossref]
[PubMed]

D. Chen, S. Zhu, H. Yi, X. Zhang, D. Chen, J. Liang, and J. Tian, “Cone beam x-ray luminescence computed tomography: a feasibility study,” Med. Phys. 40(3), 031111 (2013).

[Crossref]
[PubMed]

D. Chen, S. Zhu, H. Yi, X. Zhang, D. Chen, J. Liang, and J. Tian, “Cone beam x-ray luminescence computed tomography: a feasibility study,” Med. Phys. 40(3), 031111 (2013).

[Crossref]
[PubMed]

J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57(6), 1459–1476 (2012).

[Crossref]
[PubMed]

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).

[Crossref]
[PubMed]

I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math. 57(11), 1413–1457 (2004).

[Crossref]

I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math. 57(11), 1413–1457 (2004).

[Crossref]

M. Defrise, C. Vanhove, and X. Liu, “An algorithm for total variation regularization in high-dimensional linear problems,” Inverse Probl. 27(6), 065002 (2011).

[Crossref]

I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math. 57(11), 1413–1457 (2004).

[Crossref]

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The finite element method for the propagation of light in scattering media: boundary and source conditions,” Med. Phys. 22(11 Pt 1), 1779–1792 (1995).

[Crossref]
[PubMed]

D. Dong, S. Zhu, C. Qin, V. Kumar, J. V. Stein, S. Oehler, C. Savakis, J. Tian, and J. Ripoll, “Automated recovery of the center of rotation in optical projection tomography in the presence of scattering,” IEEE J. Biomed. Health Inform. 17(1), 198–204 (2013).

[Crossref]
[PubMed]

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006).

[Crossref]

J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57(6), 1459–1476 (2012).

[Crossref]
[PubMed]

Y. Liu, J. Ma, Y. Fan, and Z. Liang, “Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction,” Phys. Med. Biol. 57(23), 7923–7956 (2012).

[Crossref]
[PubMed]

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60(1-4), 259–268 (1992).

[Crossref]

J. Feng, C. Qin, K. Jia, S. Zhu, X. Yang, and J. Tian, “Bioluminescence tomography imaging in vivo: recent advances,” IEEE J Sel Top Quant 18(4), 1394–1402 (2012).

[Crossref]

S. Zhu, J. Tian, G. Yan, C. Qin, and J. Feng, “Cone beam micro-CT system for small animal imaging and performance evaluation,” Int. J. Biomed. Imaging 2009, 960573 (2009).

[Crossref]
[PubMed]

R. Baikejiang, Y. Zhao, B. Z. Fite, K. W. Ferrara, and C. Li, “Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method,” J. Biomed. Opt. 22(5), 55001 (2017).

[Crossref]
[PubMed]

M. A. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007).

[Crossref]

R. Baikejiang, Y. Zhao, B. Z. Fite, K. W. Ferrara, and C. Li, “Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method,” J. Biomed. Opt. 22(5), 55001 (2017).

[Crossref]
[PubMed]

T. Liu, J. Rong, P. Gao, W. Zhang, W. Liu, Y. Zhang, and H. Lu, “Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage,” J. Biomed. Opt. 23(2), 1–11 (2018).

[Crossref]
[PubMed]

P. Gao, H. Pu, J. Rong, W. Zhang, T. Liu, W. Liu, Y. Zhang, and H. Lu, “Resolving adjacent nanophosphors of different concentrations by excitation-based cone-beam X-ray luminescence tomography,” Biomed. Opt. Express 8(9), 3952–3965 (2017).

[Crossref]
[PubMed]

T. Hebert and R. Leahy, “A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors,” IEEE Trans. Med. Imaging 8(2), 194–202 (1989).

[Crossref]
[PubMed]

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The finite element method for the propagation of light in scattering media: boundary and source conditions,” Med. Phys. 22(11 Pt 1), 1779–1792 (1995).

[Crossref]
[PubMed]

J. Feng, C. Qin, K. Jia, S. Zhu, X. Yang, and J. Tian, “Bioluminescence tomography imaging in vivo: recent advances,” IEEE J Sel Top Quant 18(4), 1394–1402 (2012).

[Crossref]

Z. Tian, X. Jia, K. Yuan, T. Pan, and S. B. Jiang, “Low-dose CT reconstruction via edge-preserving total variation regularization,” Phys. Med. Biol. 56(18), 5949–5967 (2011).

[Crossref]
[PubMed]

Z. Tian, X. Jia, K. Yuan, T. Pan, and S. B. Jiang, “Low-dose CT reconstruction via edge-preserving total variation regularization,” Phys. Med. Biol. 56(18), 5949–5967 (2011).

[Crossref]
[PubMed]

E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. XRay Sci. Technol. 14, 119–139 (2006).

D. Dong, S. Zhu, C. Qin, V. Kumar, J. V. Stein, S. Oehler, C. Savakis, J. Tian, and J. Ripoll, “Automated recovery of the center of rotation in optical projection tomography in the presence of scattering,” IEEE J. Biomed. Health Inform. 17(1), 198–204 (2013).

[Crossref]
[PubMed]

T. Hebert and R. Leahy, “A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors,” IEEE Trans. Med. Imaging 8(2), 194–202 (1989).

[Crossref]
[PubMed]

J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57(6), 1459–1476 (2012).

[Crossref]
[PubMed]

R. Baikejiang, Y. Zhao, B. Z. Fite, K. W. Ferrara, and C. Li, “Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method,” J. Biomed. Opt. 22(5), 55001 (2017).

[Crossref]
[PubMed]

W. Zhang, D. Zhu, M. Lun, and C. Li, “Multiple pinhole collimator based X-ray luminescence computed tomography,” Biomed. Opt. Express 7(7), 2506–2523 (2016).

[Crossref]
[PubMed]

J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57(6), 1459–1476 (2012).

[Crossref]
[PubMed]

D. Chen, S. Zhu, H. Yi, X. Zhang, D. Chen, J. Liang, and J. Tian, “Cone beam x-ray luminescence computed tomography: a feasibility study,” Med. Phys. 40(3), 031111 (2013).

[Crossref]
[PubMed]

Y. Liu, J. Ma, Y. Fan, and Z. Liang, “Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction,” Phys. Med. Biol. 57(23), 7923–7956 (2012).

[Crossref]
[PubMed]

X. Liu, Q. Liao, and H. Wang, “Fast X-ray luminescence computed tomography imaging,” IEEE Trans. Biomed. Eng. 61(6), 1621–1627 (2014).

[Crossref]
[PubMed]

X. Liu, Q. Liao, and H. Wang, “In vivo x-ray luminescence tomographic imaging with single-view data,” Opt. Lett. 38(22), 4530–4533 (2013).

[Crossref]
[PubMed]

G. Zhang, F. Liu, J. Liu, J. Luo, Y. Xie, J. Bai, and L. Xing, “Cone beam x-ray luminescence computed tomography based on Bayesian method,” IEEE Trans. Med. Imaging 36(1), 225–235 (2017).

[Crossref]
[PubMed]

G. Zhang, F. Liu, J. Liu, J. Luo, Y. Xie, J. Bai, and L. Xing, “Cone beam x-ray luminescence computed tomography based on Bayesian method,” IEEE Trans. Med. Imaging 36(1), 225–235 (2017).

[Crossref]
[PubMed]

T. Liu, J. Rong, P. Gao, W. Zhang, W. Liu, Y. Zhang, and H. Lu, “Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage,” J. Biomed. Opt. 23(2), 1–11 (2018).

[Crossref]
[PubMed]

P. Gao, H. Pu, J. Rong, W. Zhang, T. Liu, W. Liu, Y. Zhang, and H. Lu, “Resolving adjacent nanophosphors of different concentrations by excitation-based cone-beam X-ray luminescence tomography,” Biomed. Opt. Express 8(9), 3952–3965 (2017).

[Crossref]
[PubMed]

T. Liu, J. Rong, P. Gao, W. Zhang, W. Liu, Y. Zhang, and H. Lu, “Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage,” J. Biomed. Opt. 23(2), 1–11 (2018).

[Crossref]
[PubMed]

P. Gao, H. Pu, J. Rong, W. Zhang, T. Liu, W. Liu, Y. Zhang, and H. Lu, “Resolving adjacent nanophosphors of different concentrations by excitation-based cone-beam X-ray luminescence tomography,” Biomed. Opt. Express 8(9), 3952–3965 (2017).

[Crossref]
[PubMed]

X. Liu, Q. Liao, and H. Wang, “Fast X-ray luminescence computed tomography imaging,” IEEE Trans. Biomed. Eng. 61(6), 1621–1627 (2014).

[Crossref]
[PubMed]

X. Liu, Q. Liao, and H. Wang, “In vivo x-ray luminescence tomographic imaging with single-view data,” Opt. Lett. 38(22), 4530–4533 (2013).

[Crossref]
[PubMed]

M. Defrise, C. Vanhove, and X. Liu, “An algorithm for total variation regularization in high-dimensional linear problems,” Inverse Probl. 27(6), 065002 (2011).

[Crossref]

Y. Liu, J. Ma, Y. Fan, and Z. Liang, “Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction,” Phys. Med. Biol. 57(23), 7923–7956 (2012).

[Crossref]
[PubMed]

T. Liu, J. Rong, P. Gao, W. Zhang, W. Liu, Y. Zhang, and H. Lu, “Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage,” J. Biomed. Opt. 23(2), 1–11 (2018).

[Crossref]
[PubMed]

P. Gao, H. Pu, J. Rong, W. Zhang, T. Liu, W. Liu, Y. Zhang, and H. Lu, “Resolving adjacent nanophosphors of different concentrations by excitation-based cone-beam X-ray luminescence tomography,” Biomed. Opt. Express 8(9), 3952–3965 (2017).

[Crossref]
[PubMed]

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).

[Crossref]
[PubMed]

G. Zhang, F. Liu, J. Liu, J. Luo, Y. Xie, J. Bai, and L. Xing, “Cone beam x-ray luminescence computed tomography based on Bayesian method,” IEEE Trans. Med. Imaging 36(1), 225–235 (2017).

[Crossref]
[PubMed]

Y. Lv, J. Tian, W. Cong, G. Wang, J. Luo, W. Yang, and H. Li, “A multilevel adaptive finite element algorithm for bioluminescence tomography,” Opt. Express 14(18), 8211–8223 (2006).

[Crossref]
[PubMed]

Y. Liu, J. Ma, Y. Fan, and Z. Liang, “Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction,” Phys. Med. Biol. 57(23), 7923–7956 (2012).

[Crossref]
[PubMed]

B. K. Natarajan, “Sparse approximate solutions to linear systems,” SIAM J. Comput. 24(2), 227–234 (1995).

[Crossref]

M. A. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007).

[Crossref]

D. Dong, S. Zhu, C. Qin, V. Kumar, J. V. Stein, S. Oehler, C. Savakis, J. Tian, and J. Ripoll, “Automated recovery of the center of rotation in optical projection tomography in the presence of scattering,” IEEE J. Biomed. Health Inform. 17(1), 198–204 (2013).

[Crossref]
[PubMed]

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60(1-4), 259–268 (1992).

[Crossref]

Z. Tian, X. Jia, K. Yuan, T. Pan, and S. B. Jiang, “Low-dose CT reconstruction via edge-preserving total variation regularization,” Phys. Med. Biol. 56(18), 5949–5967 (2011).

[Crossref]
[PubMed]

E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. 53(17), 4777–4807 (2008).

[Crossref]
[PubMed]

E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. XRay Sci. Technol. 14, 119–139 (2006).

C. M. Carpenter, G. Pratx, C. Sun, and L. Xing, “Limited-angle x-ray luminescence tomography: methodology and feasibility study,” Phys. Med. Biol. 56(12), 3487–3502 (2011).

[Crossref]
[PubMed]

G. Pratx, C. M. Carpenter, C. Sun, and L. Xing, “X-ray luminescence computed tomography via selective excitation: a feasibility study,” IEEE Trans. Med. Imaging 29(12), 1992–1999 (2010).

[Crossref]
[PubMed]

C. M. Carpenter, C. Sun, G. Pratx, R. Rao, and L. Xing, “Hybrid x-ray/optical luminescence imaging: characterization of experimental conditions,” Med. Phys. 37(8), 4011–4018 (2010).

[Crossref]
[PubMed]

P. Gao, H. Pu, J. Rong, W. Zhang, T. Liu, W. Liu, Y. Zhang, and H. Lu, “Resolving adjacent nanophosphors of different concentrations by excitation-based cone-beam X-ray luminescence tomography,” Biomed. Opt. Express 8(9), 3952–3965 (2017).

[Crossref]
[PubMed]

D. Dong, S. Zhu, C. Qin, V. Kumar, J. V. Stein, S. Oehler, C. Savakis, J. Tian, and J. Ripoll, “Automated recovery of the center of rotation in optical projection tomography in the presence of scattering,” IEEE J. Biomed. Health Inform. 17(1), 198–204 (2013).

[Crossref]
[PubMed]

J. Feng, C. Qin, K. Jia, S. Zhu, X. Yang, and J. Tian, “Bioluminescence tomography imaging in vivo: recent advances,” IEEE J Sel Top Quant 18(4), 1394–1402 (2012).

[Crossref]

S. Zhu, J. Tian, G. Yan, C. Qin, and J. Feng, “Cone beam micro-CT system for small animal imaging and performance evaluation,” Int. J. Biomed. Imaging 2009, 960573 (2009).

[Crossref]
[PubMed]

C. M. Carpenter, C. Sun, G. Pratx, R. Rao, and L. Xing, “Hybrid x-ray/optical luminescence imaging: characterization of experimental conditions,” Med. Phys. 37(8), 4011–4018 (2010).

[Crossref]
[PubMed]

D. Dong, S. Zhu, C. Qin, V. Kumar, J. V. Stein, S. Oehler, C. Savakis, J. Tian, and J. Ripoll, “Automated recovery of the center of rotation in optical projection tomography in the presence of scattering,” IEEE J. Biomed. Health Inform. 17(1), 198–204 (2013).

[Crossref]
[PubMed]

T. Liu, J. Rong, P. Gao, W. Zhang, W. Liu, Y. Zhang, and H. Lu, “Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage,” J. Biomed. Opt. 23(2), 1–11 (2018).

[Crossref]
[PubMed]

P. Gao, H. Pu, J. Rong, W. Zhang, T. Liu, W. Liu, Y. Zhang, and H. Lu, “Resolving adjacent nanophosphors of different concentrations by excitation-based cone-beam X-ray luminescence tomography,” Biomed. Opt. Express 8(9), 3952–3965 (2017).

[Crossref]
[PubMed]

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60(1-4), 259–268 (1992).

[Crossref]

D. Dong, S. Zhu, C. Qin, V. Kumar, J. V. Stein, S. Oehler, C. Savakis, J. Tian, and J. Ripoll, “Automated recovery of the center of rotation in optical projection tomography in the presence of scattering,” IEEE J. Biomed. Health Inform. 17(1), 198–204 (2013).

[Crossref]
[PubMed]

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The finite element method for the propagation of light in scattering media: boundary and source conditions,” Med. Phys. 22(11 Pt 1), 1779–1792 (1995).

[Crossref]
[PubMed]

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).

[Crossref]
[PubMed]

E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. 53(17), 4777–4807 (2008).

[Crossref]
[PubMed]

E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. XRay Sci. Technol. 14, 119–139 (2006).

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